Method and system for extracting lower limb vasculature

ABSTRACT

The present disclosure relates to systems and methods for extracting a vessel of a lower limb. The methods may include obtaining an original image including a plurality of image data, in some embodiments, each of the plurality of image data may correspond to a pixel (or a voxel), the plurality of image data may include a target data set, the target data set may represent a first structure; extracting a first reference data set from the plurality of image data, in some embodiments, the first reference data set may include the target data set and a second reference data set, the second reference data set may include data of a second structure; extracting the second reference data set from the plurality of image data; and obtaining the target data set based on the first reference data set and the second reference data set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to the following application:

Chinese Application No. 201610617709.5, filed on Jul. 30, 2016.

The content of the above application is incorporated herein by referenceby its entirety.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods forextracting a vessel, and in particular, to systems and methods forextracting a vessel based on the skeleton segmentation and the vesselsegmentation of an angiographic image.

BACKGROUND

Angiography plays an important role in the medical field. Angiography isone of the important methods to diagnose vascular disease. It candiagnose a variety of diseases such as aneurysm, vascular stenosis, andvascular calcification. The angiography techniques include, for example,Digital Subtraction Angiography (DSA), Magnetic Resonance Angiography(MRA), Computed Tomography Angiography (CTA), or the like, or anycombination thereof. The segmentation of vessels from other tissues isan important step in angiography. Vessel extraction (or “segmentation”,“recognition”, “confirmation”) based on angiographic images may providethe basis for the diagnosis, treatment, evaluation, virtual surgery andsurgical guidance of the disease, and/or help to calculation of vesseldiameter, quantitative analysis of vessel images, etc. Since the pixelvalues or voxel values of vessels and other tissues such as bones in theangiographic images have situations such as approximation, overlappingto a certain extent, in the processing of vessel extraction, bones, andother tissues are easily extracted as vessels, resulting in excessiveextraction; or small vessels are removed as non-vascular tissues,resulting in incomplete extraction, and so on. In order to improve theprecision of vessel extraction, a variety of methods can be used tosegment a vessel.

SUMMARY

According to an aspect of the present disclosure, a method forextracting a vessel of a lower limb is provided. The method may includeobtaining an original image including a plurality of image data, in someembodiments, each of the plurality of image data may correspond to apixel (or a voxel), the plurality of image data may include a targetdata set, the target data set may represent a first structure;extracting a first reference data set from the plurality of image data,in some embodiments, the first reference data set may include the targetdata set and a second reference data set, the second reference data setmay include data of a second structure; extracting the second referencedata set from the plurality of image data; and obtaining the target dataset based on the first reference data set and the second reference dataset.

According to another aspect of the present disclosure, a non-transitorycomputer-readable medium is provided. The non-transitory computerreadable medium may include executable instructions. When at least oneprocessor executes the instructions, the at least one processor may becaused to effectuate the method for extracting a vessel of the lowerlimb. The method may include obtaining an original image including aplurality of image data, in some embodiments, each of the plurality ofimage data may correspond to a pixel (or a voxel), the plurality ofimage data may include a target data set, the target data set mayrepresent a first structure; extracting a first reference data set fromthe plurality of image data, in some embodiments, the first referencedata set may include the target data set and a second reference dataset, the second reference data set may include data of a secondstructure; extracting the second reference data set from the pluralityof image data; and obtaining the target data set based on the firstreference data set and the second reference data set.

According to another aspect of the present disclosure, a system relatedto extracting a vessel of the lower limb is provided. The system mayinclude at least one processor and the executable instructions. Whenexecuting the executable instructions, the at least one processor may beconfigured to cause the system to perform a method for extracting avessel of a lower limb is provided. The method may include obtaining anoriginal image including a plurality of image data, in some embodiments,each of the plurality of image data may correspond to a pixel (or avoxel), the plurality of image data may include a target data set, thetarget data set may represent a first structure; extracting a firstreference data set from the plurality of image data, in someembodiments, the first reference data set may include the target dataset and a second reference data set, the second reference data set mayinclude data of a second structure; extracting the second reference dataset from the plurality of image data; and obtaining the target data setbased on the first reference data set and the second reference data set.

In some embodiments, the obtaining the target data set may includeobtaining a frame data set based on the first reference data set and thesecond reference data set, in some embodiments, the frame data set maybe a subset of the target data set; and performing at least one datasupplement operation on the frame data set to obtain the target data set

In some embodiments, the first structure may include a vessel, thesecond structure may include a skeleton, the target data set may includevessel data, the first reference data set may include vessel data andskeleton data, the second reference data set may include skeleton data,and the frame data set may include data of broken vessel segments.

In some embodiments, the extracting the first reference data set mayinclude determining at least one connected domain CD1 in the originalimage; determining a first seed point based on the at least oneconnected domain CD1; and performing a regional growth on the originalimage based on the first seed point and a first threshold to obtain afirst image, in some embodiments, pixels or voxels in the first imageand data in the first reference data set are bijective.

In some embodiments, the determining the first seed point may includedetermining values of a boundary distance field of the at least oneconnected domain CD1 to obtain a data set pfield-1; determining acircularity degree of the at least one connected domain CD1 based on thedata set pfield-1; determining a target connected domain based on the atleast one connected domain CD1; and determining the first seed pointbased on the target connected domain.

In some embodiments, the determining the circularity degree of the atleast one connected domain CD1 may include determining a radius of theat least one connected domain CD1 based on the data set pfield-1;determining a circular area of the at least one connected domain CD1based on the radius of the at least one connected domain CD1; anddetermining the circularity degree of the at least one connected domainCD1 based on the circular area of the at least one connected domain CD1and an actual area of the at least one connected domain CD1.

In some embodiments, the extracting the second reference data set mayinclude calculating a boundary distance field based on the first imageto obtain a data set pfield-2; segmenting the first structure based onthe data set pfield-2 to obtain a vessel mask; subtracting the vesselmask from the first image to obtain a first subtraction image;segmenting the second structure based on the first subtraction image toobtain a first skeleton mask; and obtaining a second skeleton mask basedon the original image and the first skeleton mask, wherein pixels orvoxels in the second skeleton mask and data in the second reference dataset are bijective.

In some embodiments, the obtaining the vessel may include performing aregional growth on the first image based on the first seed point toobtain a first vessel mask; and dilating the first vessel mask to obtainthe vessel mask.

In some embodiments, the obtaining the first skeleton mask may includecalculating a boundary distance field based on the first subtractionimage to obtain a data set pfield-3; determining a skeleton seed pointbased on the data set pfield-3; and performing a regional growth on thefirst subtraction image based on the skeleton seed point to obtain thefirst skeleton mask.

In some embodiments, the obtaining the second skeleton mask may includedetermining a first skeleton region based on the first skeleton mask;and dilating the first skeleton region to obtain a first temporaryskeleton mask.

In some embodiments, the obtaining the second skeleton mask may includeperforming a regional growth on the original image based on a secondthreshold to obtain a second image; filling the second image to obtain afilled second image; obtaining a superimposition image based on thefirst temporary skeleton mask and the filled second image; andperforming a closing operation on at least one connected domain CD2 inthe superimposition image to obtain the second skeleton mask.

In some embodiments, the determining the first skeleton region mayinclude eroding the first skeleton mask to obtain at least one connecteddomain CD3; and determining the first skeleton region based on the atleast one connected domain CD3.

In some embodiments, the determining the first skeleton region mayfurther include designating a connected domain with the maximum area orthe maximum volume in the at least one connected domain CD3 as the firstskeleton area.

In some embodiments, the obtaining the frame data set may includesubtracting the second skeleton mask from the first image to obtain asecond subtraction image, wherein pixels or voxels in the secondsubtraction image and data in the frame data set are bijective.

In some embodiments, the performing at least one data supplementoperation on the frame data set may include selecting a second seedpoint from the frame data set; and performing a regional growth based ona second threshold and the second seed point to obtain the target dataset.

In some embodiments, the performing at least one data supplementoperation on the frame data set may include extracting a center line ofa vessel based on the second subtraction image; and generating thevessel based on the center line of the vessel.

In some embodiments, the extracting the center line of the vessel mayinclude calculating a boundary distance field based on the secondsubtraction image; obtaining a first vessel growing point and a secondvessel growing point based on the boundary distance field; andextracting the center line of the vessel using the shortest routealgorithm based on the first vessel growing point and the second vesselgrowing point.

In some embodiments, the extracting the center line of the vessel mayfurther include determining a second skeleton region in the secondskeleton mask; and extracting the center line of the vessel by excludingthe second skeleton region.

In some embodiments, the performing at least one data supplementoperation on the frame data set may further include ranking datacorresponding to the pixels or the voxels in the second subtractionimage.

In some embodiments, the performing at least one data supplementoperation on the frame data set may further include positioning a regionof the second structure based on image data of the original image;segmenting the second structure based on the region of the secondstructure to obtain a data set for growth control; and expanding thedata set for growth control to obtain an expanded data set for growthcontrol, in some embodiments, the expanded data set for growth controlmay limit the growing of the vessel.

In some embodiments, the positioning the region of the second structuremay include obtaining reference image data; extracting a feature of thereference image data; generating a template histogram based on thefeature of the reference image data; extracting a feature of the imagedata based on the feature of the reference image data; generating asample histogram based on the feature of the image data; and positioningthe region of the second structure based on a similarity between thetemplate histogram and the sample histogram.

In some embodiments, the reference image data includes image data of astructure similar to the second structure.

In some embodiments, the feature of the reference image data includes anumber or a sectional area of data of the second structure.

In some embodiments, the feature of the image data includes a number ora sectional area of data of the second structure.

In some embodiments, the reference image data and the image data aredata from different detection objects.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 illustrates a schematic diagram of an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram of the data processing system according tosome embodiments of the present disclosure;

FIG. 3 is a flowchart illustrating an exemplary process for imagegeneration according to some embodiments of the present disclosure;

FIG. 4-A is a schematic diagram illustrating an exemplary processingmodule according to some embodiments of the present disclosure;

FIG. 4-B is a flowchart illustrating an exemplary process for image dataprocessing according to some embodiments of the present disclosure;

FIG. 5-A is a schematic diagram illustrating an exemplary vesselsegmentation submodule according to some embodiments of the presentdisclosure;

FIG. 5-B is a flowchart illustrating an exemplary process for segmentinga vessel according to some embodiments of the present disclosure;

FIG. 6-A is a schematic diagram illustrating an exemplary vesselsegmentation submodule according to some embodiments of the presentdisclosure;

FIG. 6-B is a flowchart illustrating an exemplary process for segmentinga vessel according to some embodiments of the present disclosure;

FIG. 7-A is a schematic diagram illustrating an exemplary skeletonextraction unit according to some embodiments of the present disclosure;

FIG. 7-B is a flowchart of an exemplary process for extracting askeleton according to some embodiments of the present disclosure;

FIG. 7-C is a flowchart illustrating an exemplary process for obtaininga first image according to some embodiments of the present disclosure;

FIG. 7-D is a flowchart illustrating an exemplary process fordetermining a seed point according to some embodiments of the presentdisclosure;

FIG. 7-E is a flowchart illustrating an exemplary process for obtaininga vessel mask according to some embodiments of the present disclosure;

FIG. 7-F is a flowchart illustrating an exemplary process for obtaininga first skeleton mask according to some embodiments of the presentdisclosure;

FIG. 7-G is a flowchart illustrating an exemplary process for obtaininga second skeleton mask according to some embodiments of the presentdisclosure;

FIG. 7-H is a schematic diagram illustrating an exemplary fillingoperation on a skeleton region according to some embodiments of thepresent disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for generating avessel according to some embodiments of the present disclosure;

FIG. 9 is a schematic diagram illustrating an exemplary vesselgeneration unit according to some embodiments of skeleton the presentdisclosure;

FIG. 10-A is a flowchart illustrating an exemplary process forsupplementing tibia vessel data according to some embodiments of thepresent disclosure;

FIG. 10-B is a flowchart illustrating an exemplary process forsupplementing foot vessel data according to some embodiments of thepresent disclosure;

FIG. 10-C is a flowchart illustrating an exemplary process forperforming a supplement operation on a data supplement region accordingto some embodiments of the present disclosure;

FIG. 10-D is a flowchart illustrating an exemplary process forsupplementing ilium vessel data according to some embodiments of thepresent disclosure;

FIG. 10-E is a flowchart illustrating an exemplary process forgenerating a vessel according to some embodiments of the presentdisclosure;

FIG. 11 is a flowchart illustrating an exemplary process for positioninga specific site of a human body in a medicine image according to someembodiments of the present disclosure;

FIG. 12 is a schematic diagram illustrating exemplary results of fivevessels of lower limbs according to some embodiments of the presentdisclosure;

FIG. 13 is a schematic diagram illustrating an exemplary second vesselmask according to some embodiments of the present disclosure; and

FIG. 14 is a schematic diagram illustrating an exemplary result ofvessel extraction according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In order to illustrate the technical solutions related to theembodiments of the present disclosure, brief introduction of thedrawings referred to the description of the embodiments is providedbelow. Obviously, drawings described below are only some examples orembodiments of the present disclosure. Those having ordinary skills inthe art, without further creative efforts, may apply the presentdisclosure to other similar scenarios according to these drawings.Unless stated otherwise or obvious from the context, the same referencenumeral in the drawings refers to the same structure and operation.

As used in the disclosure and the appended claims, the singular forms“a,” “an,” and “the” include plural referents unless the content clearlydictates otherwise. It will be further understood that the terms“comprises,” “comprising,” “includes,” and/or “including” when used inthe disclosure, specify the presence of stated steps and elements, butdo not preclude the presence or addition of one or more other steps andelements.

According to some embodiments of the present disclosure, flowcharts areused to illustrate the operations performed by the system. It is to beexpressly understood, the operations above or below may or may not beimplemented in order. Conversely, the operations may be performed ininverted order, or simultaneously. Besides, one or more other operationsmay be added to the flowcharts, or one or more operations may be omittedfrom the flowchart.

During the processing of image data, the terms “image segmentation,”“image extraction,” and “image classification” may be usedinterchangeably and may all represent selecting an image that satisfiesa condition from a large area. In some embodiments, an imaging systemmay include one or more configurations. The configurations may includedigital subtraction angiograph (DSA), magnetic resonance angiography(MRA), computed tomography (CT), computed tomography angiograph (CTA),ultrasonic scanning (US), positron emission tomography (PET),single-photon emission computerized tomography (SPECT), SPECT-MR,CT-PET, CE-SPECT, DSA-MR, PET-MR, PET-US, SPECT-US, US-CT, US-MR,X-ray-CT, X-ray-US, or the like, or a combination thereof. In someembodiments, a scanning target for imaging may be an organ, an organism,an object, an injured part, a tumor, or the like, or a combinationthereof. In some embodiments, the scanning target for imaging may be ahead, a thoracic cavity, an abdomen, an organ, a skeleton, a vessel, orthe like, or a combination thereof. In some embodiments, the scanningtarget may be vascular tissues of one or more body parts. In someembodiments, the image may be a two-dimensional image and/or athree-dimensional image. In the two-dimensional image, the delicatedistinguishable element may be a pixel. In the three-dimensional image,the delicate distinguishable element may be a voxel. In thethree-dimensional image, the image may include a series oftwo-dimensional slice images and/or two-dimensional image layers.

The image segmentation process may be performed based on a feature of apixel or a voxel in an image. In some embodiments, the feature of apixel or a voxel may include texture structure, gray degree, averagegray degree, strength, color saturation level, contrast, brightness, orthe like, or a combination thereof. In some embodiments, a spatiallocation feature of a pixel or a voxel may also be used in the imagesegmentation process.

FIG. 1 illustrates a schematic diagram of an exemplary imaging systemaccording to some embodiments of the present disclosure. An imagingsystem may include an imaging device 110, a controller 120, a dataprocessing system 130, an input/out device 140, and a network 170.

The imaging device 110 may scan a target object and generate relateddata and image. The imaging device 110 may also further process theimage based on the generated data. In some embodiments, the imagingdevice 110 may be a single device or a group of devices. In someembodiments, the imaging device 110 may be a medical imaging device, forexample, a PET device, a SPECT device, a CT device, a MRI device, or thelike. In some embodiments, the device may be used separately or incombination (e.g., a PET-CT device, a PET-MRI device or a SPECT-MRIdevice). In some embodiments, the imaging device 110 may include ascanner for scanning the target object and obtaining related information(e.g., data). Further, the imaging device 110 may be a radioactivescanning device. The radioactive scanning device may include aradioactive source that may emit radioactive rays to the target object.The radioactive rays may include a particulate ray, a photon ray, or thelike, or a combination thereof. The particulate ray may include neutron,proton, α ray, electron, μ medium, heavy ion, or the like, or acombination thereof. The photon ray may include X-ray, γ ray,ultraviolet ray, laser, or the like, or a combination thereof. In someembodiments, the photon ray may be an X-ray and the correspondingimaging device 100 may be a CT system, a digital radiography (DR)system, a multimodal medical imaging system, or the like, or acombination thereof. In some embodiments, the multimodal medical imagingsystem may include a CT-PET system, a SPECT-MRI system, or the like, ora combination thereof. In some embodiments, the imaging device 110 mayinclude a ray generation unit and a ray detection unit (not shown). Forexample, the imaging device 110 may include a photon detector forgenerating rays, and/or detecting rays, etc. For example, the photondetector may generate a photon used for scanning the target object ordetect a photon that has scanned the target object. In some embodiments,the imaging device 110 may be a CT imaging system or a multimodalmedical imaging system based on CT whose photon detector may include anX-ray detector.

Image data generated by the imaging device 110 may be processedaccording to a coordinate system rule. In some embodiments, the imagedata may be processed according to the Descartes rectangular coordinatesystem. In the Descartes rectangular coordinate system, the x-directionmay refer to a direction of a frontal axis (a direction of anintersecting line of a coronal section and a horizontal plane). Forexample, the direction of a frontal axis may refer to a direction froman imaged object's right side to the imaged object's left side or adirection from an imaged object's left side to the imaged object's rightside. The y-direction may refer to a direction of an anteroposterioraxis (a direction of an intersecting line of a vertical plane and ahorizontal plane). For example, the direction of an anteroposterior axismay refer to a direction from an imaged object's back part to the imagedobject's front part or a direction from an imaged object's front part tothe imaged object's back part. The z-direction may refer to a directionof a vertical axis (a direction of an intersecting line of a coronalsection and a vertical plane). For example, the direction of thevertical axis may refer to a direction from an imaged object's top tothe imaged object's bottom or a direction from an imaged object's bottomto the imaged object's top. It should be noted that the X coordinate,the Y coordinate, and the Z coordinate shown in FIG. 1 are provided forillustration purposes, and not to intended to limit the scope of the Xcoordinate, the Y coordinate, and the Z coordinate.

The controller 120 may control the imaging device 110, the input/outputdevice 140, and/or the data processing system 130. In some embodiments,the controller 120 may control the X-ray generation unit and/or theX-ray detection unit in the imaging device 110. The controller 120 mayreceive information from the imaging device 110, the input/output device140 and/or the data processing system 130 or transmit information to thesystem(s)/device(s) above. In some embodiments, the controller 120 mayreceive data related to the target object or an image signal from theimaging device 110. The controller 120 may transmit the data related tothe target object or the image signal to the data processing system 130.The controller 120 may receive processed data or reconstructed imagefrom the data processing system 130. The controller 120 may transmit theprocessed data or the reconstructed image to the input/output device140. In some embodiments, the controller 120 may include a computer, aprogram, an algorithm, software, a storage device, interfaces, or thelike. The interfaces may include interfaces among the imaging device110, the input/output device 140, the data processing system 130 and/orother modules or units in the imaging system.

In some embodiments, the controller 120 may receive instructions from auser (e.g., a doctor, an imaging technician). The controller 120 mayreceive the instructions from the user via the input/output device 140.The controller 120 may receive the instructions or convert theinstructions to control the imaging device 110, the input/output device140, and/or the data processing system 130. For example, the controller120 may process data input by a user via the input/output device 140 andtransform the data into one or more corresponding instructions. Theinstructions may include a scanning time, positioning information of ascanned target, a rotating velocity of a frame, a scanning parameter, orthe like, or a combination thereof. The controller 120 may control thedata processing system 130 to select different algorithms to process theimage data.

The data processing system 130 may process information received from theimaging device 110, the controller 120, the network 170, and/or theinput/output device 140. In some embodiments, the data processing system130 may generate one or more CT images based on the information. Thedata processing system 130 may transmit the images to the input/outputdevice 140. The data processing system 130 may perform variousoperations related to data processing. The various operations mayinclude data preprocessing, data transformation, data cleaning, datafitting, data weighting processing, or the like, or a combinationthereof. The data processing system 130 may implement the dataprocessing based on different algorithm routines. The algorithm routinesmay include Fourier transformation principle, filtering back projectionprinciple, iteration reconstruction, histogram swelling calculation,image data function optimization, level set function calculation, or thelike, or a combination thereof. In some embodiments, the data processingsystem 130 may process data related to an image of a vessel. Forexample, the data processing system 130 may recognize skeleton in thechest and the abdomen 150, a vessel of the abdominal 160, a vessel of alower limb, a center line of a vessel, or vessels in other parts. Insome embodiments, the data processing system 130 may position a vesselor a skeleton in an image using multiple algorithm routines or methods,for example, the data processing system 130 may position a rib, anilium, a sacrum, a tibia, an iliac artery, or the like.

In some embodiments, the data processing system 130 may generate acontrol signal related to the imaging device 110. In some embodiments,processed (and/or unprocessed) data result processed by the dataprocessing system 130 may be transmitted to other modules or units inthe system. The other modules or units may refer to a database (notshown), a terminal receiver of the network 170 (not shown). The dataprocessing system 130 may be capable of storing the unprocessed dataand/or processed data. In some embodiments, the data informationcorresponding to the data processing system 130 may be furtherprocessed, transmitted to storage to be stored, and/or transmitted to aterminal.

The input/output device 140 may receive, transmit or displayinformation. In some embodiments, the input/output device 140 mayinclude a keyboard, a touch-sensitive device, a mouse, an audio inputdevice, an image input device, a remote control device, or the like, ora combination thereof. The input/output device 140 may include aprogram, software, an algorithm, data, a signal, a text, a number, animage, audio, or the like, or a combination thereof. In someembodiments, a user may input a plurality of initial parameters or setan initialization condition for corresponding image processing. In someembodiments, the input information may be from an external data source(e.g., a soft disk, a rigid disk, a light disk, a storage chip, a wiredterminal, a wireless terminal, or a combination thereof). Theinput/output device 140 may receive information from other modules orunits in the system and/or transmit information to other modules orunits in the system. In some embodiments, the input/output device 140may transmit information to a terminal (e.g., a display screen, aprinter, a storage device, a computing device, or a combination thereof)to perform corresponding operations. Specifically, in some embodiments,the input/output device 140 may include a graphical user interface usedto display information on steps of the imaging process or an imageprocessing result (e.g., an image histogram, a skeleton mask, a vesselmask, the images related to a vessel by image transformation, or acombination thereof). The graphical user interface may provide anotification for a user to input a parameter, and/or direct the user toparticipate the data processing process (e.g., start or stop theprocessing, select or amend operating parameters, select or amend analgorithm, amend a program, exit the system, upgrade the system, updatethe system).

The network 170 may be a single network or a combination of a pluralityof different networks. For example, the network 170 may include a localarea network (LAN), a wide area network (WAN), a public network, aprivate network, an exclusive network, a public switched telephonenetwork (PSTN), the Internet, a wireless network, a virtual network, orthe like, or a combination thereof. The network 170 may also include aplurality of network access points. The wired network may be a networkthat is implemented via, for example, a metal cable, a composite cable,one or more access points, or the like, or a combination thereof. Thewireless network may be a network that is implemented via, for example,Bluetooth, LAN, WAN, ZigBee, near field communication (NFC), or thelike, or a combination thereof. The network 170 may be suitable in thescope of the present disclosure but is not limited the description.

In some embodiments, the imaging device 110, the controller 120, thedata processing system 130, and the input/output device 140 may beconnected with each other directly or indirectly. In some embodiments,the imaging device 110, the controller 120, the data processing system130, and the input/output device 140 may be connected with each otherdirectly via the network 170. In some embodiments, the imaging device110, the controller 120, the data processing system 130, and theinput/output device 140 may be connected with each other indirectly byone or more intermediary units (not shown). The intermediary units maybe tangible or intangible (e.g., wireless electric wave, optical wave,acoustic wave, electromagnetic wave, or the like, or a combinationthereof). Different modules and units may be connected to each other bya wireless connection and/or a wired connection.

The CT system is merely an embodiment of the imaging device 110, and thepresent disclosure is not limited in the scope of the embodiment. The CTsystem may be used in various application scenarios such as medicine,industry, etc. Furthermore, the scanning results of the CT system may beused in various analyses, for example, diagnose analysis, safety scan,defect detection, quantitative analysis, failure analysis, or the like,or a combination thereof.

It should be noted that the above description of the image processingsystem is provided for the purpose of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, modules may be combined in various ways, or connectedwith other modules as sub-systems. Various variations and modificationsmay be conducted under the teaching of the present disclosure. However,those variations and modifications may not depart from the spirit andscope of this disclosure.

FIG. 2 is a schematic diagram of the data processing system 130according to some embodiments of the present disclosure. The dataprocessing system 130 may include one or more data acquisition modules210, storage modules 220, one or more display modules 230, and one ormore processing modules 240. The modules may be connected with eachother directly (and/or indirectly).

The data acquisition module 210 may obtain data. The obtained data maybe from the imaging device 110 and/or the controller 120. In someembodiments, the data may be obtained via the network 170 from anexternal source. The data may be three-dimensional data and/ortwo-dimensional data. The data may be data that is obtained from aspecific part according to detection requirements. The data may includea panoramic scan of a target object, a chest, a lung, a lower limb, abronchus, a skeleton, a vessel, a nerve distribution of a target object,or the like, or a combination thereof. In some embodiments, the data maybe angiographic data. In some embodiments, the data acquisition module210 may obtain original data of an image of a vessel, data of aprocessed image of the vessel, or a processing parameter of the image ofthe vessel, etc.

The storage module 220 may store data or information. The stored data orinformation may be from the imaging device 110, and/or the controller120, and/or other modules/units of the data processing system 130 (e.g.,the data acquisition module 210, the display module 230, the processingmodule 240, other modules (not shown). The stored data or informationmay be in various forms, for example, numerical value, signal, image,related information of a target object, command, algorithm, program, orthe like, or a combination thereof. In some embodiments, the stored dataor information may be an image of a vessel, a parameter of the image ofthe vessel, data of the image of the vessel, data of processed image ofthe vessel, a program and/or an algorithm used to process the image ofthe vessel, etc.

The storage module 220 may include a rigid disk, a soft disk, a randomaccess memory (RAM), a dynamic random access memory (DRAM), a staticrandom access memory (SRAM), a bubble memory, a thin film memory, amagnetic plated wire memory, a phase change memory, a flash memory, acloud disk, or the like, or a combination thereof. The storage module220 may provide a temporary storage, that is, the storage module 220 mayload and store data for a next data processing. The storage module 220may provide a permanent storage, that is, the storage module 220 maystore final data processing results. The storage module 220 may be animmobile storage system (e.g., a disk) and/or a mobile storage system(e.g., a USB port, a port (e.g., a hot wire port), a drive (e.g., a diskdrive). The storage module 220 may be connected to one or more dataacquisition modules 210, display modules 230, processing modules 240, orother modules (not shown). In some embodiments, the storage module 220may be selectively connected to one or more virtual storage resources(e.g., a cloud storage, a virtual private network, and/or other virtualstorage resources) via the network 170.

The display module 230 may display data. The displayed data informationmay be from the data acquisition module 210, the storage module 220,and/or the processing module 240. The displayed data may be transmittedto the input/output device 140. In some embodiments, the display module230 may transmit the image data obtained by the processing module 240 toa terminal to display. In some embodiments, the display module 230 maydirectly display related data information from the network 170 or thestorage module 220. The data may be displayed in various forms includingan acoustic form (e.g., voice) and/or a vision form (e.g., text, video,graph), or the like, or a combination thereof. The displayed data may bein various forms including a numerical value, a signal, an image,related information of a target object, a command, an algorithm, aprogram, or the like, or a combination thereof. In some embodiments, thedisplay module 230 may display an image including vessel information(e.g., a histogram, a gray scale image of a vessel, an image of a vesselmask, an image of rough segmentation of a vessel, an image of precisesegmentation of a vessel, an image of skeleton segmentation)

The processing module 240 may process related data and construct animage based on the related data. The data may be from the dataacquisition module 210, the storage module 220, other modules not shownand/or an external resource obtained via the network 170. Theconstructed image may be transmitted to the display module 230, etc. Thedata processed by the processing module 240 may be data related to aspecific part of a target object. The specific part may include a heart,a vessel, a liver, a spleen, a kidney, a skeleton, or the like, or acombination thereof. For example, the processing module 240 may processdata of a vessel of an abdomen vessel and a vessel of a lower limb. Theprocessing module 240 may process the data based on a plurality ofmethods. In some embodiments, a user may select the data that needs tobe processed. For example, the user may select a vessel of a designatedpart in an image to be processed. In some embodiments, the data may beprocessed based on one or more algorithms, for example, histogramfitting, image transformation processing, data weighting processing, orthe like.

The processing module 240 may include a general processor. The generalprocessor may include but is not limited a programmed logic device(PLD), an application special integrated circuit (ASIC), amicroprocessor, a system on chip (SOC), a digital signal processor(DSP), or the like, or a combination thereof. In some embodiments, twoor more processors may be integrated into a hardware device. In someembodiments, the two or more processors may be independent of orconnected with each other. The processors may process data by aplurality of methods including by hardware, by software, by acombination of hardware and software, or the like.

It should be noted that the above description of the data processingsystem 130 is provided for the purpose of illustration, and not intendedto limit the scope of the present disclosure. For persons havingordinary skills in the art, modules may be combined in various ways, orconnected with other modules as sub-systems. Various variations andmodifications may be conducted under the teaching of the presentdisclosure. However, those variations and modifications may not departfrom the spirit and scope of this disclosure.

FIG. 3 is a flowchart illustrating an exemplary process for imagegeneration according to some embodiments of the present disclosure. Insome embodiments, the data processing system 130 may perform the imagegeneration process. The image generation process may include obtainingimage data of a measured object 310, processing the image data 320, andgenerating an image 330.

In 310, image data of a measured object may be obtained. The measuredobject may include a human body, an animal, or a part of the human bodyor the animal. For example, the part may include an organ, tissue, adiseased part, a tumor part, or the like, or a combination thereof. Forexample, the measured object may include but is not limited a head, achest, an abdomen, a heart, a liver, a spleen, a kidney, an upper limb,a lower limb, a backbone, a skeleton, a vessel, or the like, or acombination thereof. The image data of the measured object may betwo-dimensional image data or three-dimensional image data. The imagedata of the measured object may be MRA image data, CTA image data, PETimage data, or the like, or a combination thereof. In some embodiments,the data acquisition module 210 may implement 310. In some embodiments,the image data of the measured object may be obtained from the storagemodule 220. In some embodiments, the image data of the measured objectmay be obtained from an external data source via the network 170. Insome embodiments, the image data of the measured object may be obtainedfrom the input/output device 140.

In 320, the obtained image data of the measured object may be processed.The image data processing may include one or more sub-steps. In someembodiments, the processing module 240 may implement 320. In someembodiments, the image data processing may include deletion ofunreliable data or correction of a value of data. In some embodiments,the image data processing may include filtration of data noise, etc. Insome embodiments, the image data processing may include imagesegmentation, image rendering, image transformation, or the like. Insome embodiments, the image data processing may be based on one or morealgorithms, for example, swelling, corrosion, the regional growthmethod, the level set algorithm, the gradient descent method, the singlesource shortest route algorithm, or the like, or a combination thereof.

In 330, an image may be generated based on the processed image data in320. The display module 230 or the processing module 240 may implement330. The image generation operation may be based on one or morealgorithms, for example, the image transformation algorithm, the imagedisplaying algorithm, or the like, or a combination thereof. The imagetransformation algorithm may include a transformation from frequencydomain to image domain, a transformation of a gray image, or the like.The image displaying algorithm may include an algorithm of adjusting theimage's parameters including color, contrast ratio, brightness, or thelike.

It should be noted that the above descriptions about the processing ofthe image generation are provided for illustration purposes, and shouldnot be designated as the only practical embodiment. For persons havingordinary skills in the art, after understanding the general principle ofthe process for the image generation, without departing the principle,may modify or change the forms or details of the particular practicalways and steps, and further make simple deductions or substitutions, ormay make modifications or combinations of some steps without furthercreative efforts. However, those variations and modifications do notdepart the scope of the present disclosure. In some embodiments, steps320 and 330 may be combined into an independent operation. In someembodiments, before step 330, the image processing process may return to320 to further process the image data. In some embodiments, steps 330and 320 may be performed simultaneously. In some embodiments, one ormore operations may be added to the flowchart or be omitted from theflowchart. For example, a scanning operation of the measured object maybe added before step 310. The measured object may be scanned by theimaging device 110. As another example, a data storage operation may beadded among, before, or after step 310, 320, and/or 330. The data may bestored in the storage module 220.

FIG. 4-A is a schematic diagram illustrating an exemplary processingmodule 240 according to some embodiments of the present disclosure. Theprocessing module 240 may include a preprocessing submodule 410, avessel segmentation submodule 420, and a visualization submodule 430.

The preprocessing submodule 410 may preprocess image data. The imagedata preprocessing may make the data suitable for vessel segmentation.The image data preprocessing may include image normalization, imagereconstruction, image smoothing, image compression, image enhancement,image matching, image registration, image geometric correction,elimination of image mutation or noise, or the like, or any combinationthereof. In some embodiments, the preprocessing submodule 410 may beomitted.

The vessel segmentation submodule 420 may segment a vessel from an imageincluding vessels. The segmented vessel may include vessel(s) of one ormore body parts of a subject, for example, a vessel of a head and neck,a thoracic vessel, a vessel of an abdomen, a vessel of an upper limb, avessel of a lower limb, a vessel of a foot, etc. The segmented vesselmay include a vessel within an organ of the subject, for example, avessel of a brain, a vessel of a heart, a vessel of a liver, a vessel ofa spleen, a vessel of a kidney, etc. In some embodiments, the vesselsegmentation submodule 420 may segment a characteristic line of avessel. The characteristic line of a vessel may refer to one or morelines, points, or the like, or any combination thereof, such as a centerline of a vessel, a boundary line of the vessel, an endpoint of thevessel, etc. The characteristic line of the vessel may be a collectionof one or more pixels (or voxels) located within or at the boundary ofthe vessel. In some embodiments, the characteristic line of the vesselmay refer to a line located at or near the center of the vessel and/or aline representing a trend of the vessel. In some embodiments, the centerline of the vessel may refer to a line connecting pixels (or voxels)with equal distances to the vessel boundary. In some embodiments, theboundary line of the vessel may refer to a line located at a wall of thevessel or near the wall of the vessel, and may represent a boundarybetween the vessel and non-vascular parts. The vessel may include acollection of pixels within the center line of the vessel and theboundary line of the vessel. The endpoint of the vessel may refer to oneor more pixels (or voxels) at the end of the vessel. In someembodiments, the end of the vessel may be an end of the vessel inanatomy. In some embodiments, the endpoint of the vessel may be an endof the vessel within a range that is manually or automaticallydetermined, for example, an end region of the vessel within adisplayable range of an image.

In some embodiments, the vessel segmentation submodule 420 may include astorage unit for storing one or more programs or algorithms, such as athreshold-based segmentation method, an edge-based segmentation method,a region-based segmentation method, a segmentation method based oncluster analysis, a segmentation method based on Wavelet Transform, asegmentation method based on mathematical morphology, a method based onartificial neural network, a method based on genetic algorithm, and acombination method of vessels of the same or different parts.

The visualization submodule 430 may perform a visualization processingon the image data. The visualization submodule 430 may convert the datainto a visual form. The visualized image may be a gray scale image or acolor image. The visualized image may be a two-dimensional image or athree-dimensional image. The visualized image may be displayed on theinput/output device 140 or printed by a printer. In some embodiments,the image data for visualization processing may be obtained from thepreprocessing submodule 410 and/or the vessel segmentation submodule420.

FIG. 4-B is a flowchart illustrating an exemplary process for processingimage data according to some embodiments of the present disclosure. Theprocess for processing image data may include preprocessing data 440,segmenting a vessel 450, and generating an image of the vessel 460.

In 440, the data may be preprocessed. The preprocessing submodule 410may perform 440. The data preprocessing may include image smoothing,image denoising, image enhancement, or the like, or any combinationthereof. The image smoothing may be performed in the image domain or thefrequency domain. In some embodiments, pixels (or voxels) in the imagemay be processed directly by the image smoothing in the image domain. Insome embodiments, the image smoothing in the frequency domain mayinclude converting the image data in the image domain into the imagedata in the frequency domain, processing the image data in the frequencydomain, and converting the processed image data in the frequency domaininto the image data in the image domain. The image smoothing may includemedian smoothing, Gaussian smoothing, average smoothing, normalizedsmoothing, bilateral filtering smoothing, or the like, or anycombination thereof. Noise in the image may be removed by the imagedenoising. In some embodiments, the image denoising may be performedaccording to one or more denoising models, for example, Gaussianfiltering, anisotropy diffusion equation model, bilateral filtering,total variation model, Wavelet transform filtering, or non-local means,or the like.

In 450, the vessel may be segmented. The vessel segmentation submodule420 may perform 450. The vessel segmentation may be based on thepreprocessing result in 440. In some embodiments, the vesselsegmentation may be based on data obtained directly from the dataacquisition module 210, the storage module 220, or the input/outputdevice 140, or data obtained from an external data source via thenetwork 170. The vessel segmentation may be performed according to oneor more image segmentation algorithms, for example, a gradient descentmethod, a threshold method, a regional growth method, a level setmethod, region segmentation and/or merging, an edge trackingsegmentation method, a statistical pattern recognition method, a meanclustering segmentation method, a manual calibration method, a topologyrefinement method, a distance transformation method, or the like, or anycombination thereof. In some embodiments, the vessel segmentation methodmay be stored in the vessel segmentation submodule 420, the storagemodule 220, or mobile storage devices (e.g., a portable hard disk, a USBflash disk). In some embodiments, the vessel segmentation method may beobtained from one or more external data sources via the network 170.

In 460, an image of the vessel may be generated. The image of the vesselmay be generated based on the result of the segmented vessel obtained in450. The visualization submodule 430 may perform 460. In someembodiments, a color image may be generated. In some embodiments, a grayscale image may be generated. In some embodiments, step 460 may beperformed according to one or more post-processing operations. Thepost-processing operations may be based on two-dimensionalpost-processing techniques including, for example, a multi-planarrecombination technique, a cured surface reconstruction technique, avolume reconstruction technique, a volume rendering technique, or thelike, or any combination thereof. The post-processing operation may bebased on three-dimensional post-processing techniques including, forexample, a three-dimensional surface reconstruction technique, athree-dimensional volume reconstruction technique, a volume intensityprojection technique, a maximum intensity projection technique, aminimum intensity projection technique, an average density projectiontechnique, or the like, or any combination thereof. Other possibletechniques may include repair, rendering, filling, or the like, or anycombination thereof.

It should be noted that the above descriptions about the processingmodule and the process for processing image data are provided forillustration purposes, and should not be designated as the onlypractical embodiment. Each submodule may be implemented by one or morecomponents, and the function of each submodule is not limiting. Eachsubmodule may be added or omitted in specific scenarios. For personshaving ordinary skills in the art, after understanding the generalprinciple of the process for processing image data without departing theprinciple, may modify or change the forms or details of the particularpractical ways and steps, and further make simple deductions orsubstitutions, or may make modifications or combinations of some stepswithout further creative efforts. However, those variations andmodifications do not depart the scope of the present disclosure. Forexample, the preprocessing submodule 410 and/or the preprocessing data440 may be omitted. As another example, 440 and 450 may be combined intoone operation. As yet another example, 450 and 460 may be performedsimultaneously or alternately.

FIG. 5-A is a schematic diagram illustrating an exemplary vesselsegmentation submodule 420 according to some embodiments of the presentdisclosure. The vessel segmentation submodule 420 may include an imagedata input/output unit 501, a reference data extraction unit 502, atarget data extraction unit 503, and/or a frame data extraction unit504.

The image data input/output unit 501 may input and/or output image data.The image data may refer to values of pixels (or voxels) in the image.The image data may include vessel data and/or non-vessel data (e.g.,skeleton data, fat data, data of other types of tissue). In someembodiments, the input image data may be data obtained by the dataacquisition module 210, data preprocessed by the preprocessing submodule410, data input by the input/out device 140, and/or data obtained viathe network 170. The data output by the image data input/output unit 501may be vessel data after the vessel segmentation processing, and datagenerated in the intermediate process of the vessel segmentation process(e.g., skeleton data, fat data, data of other tissues). In someembodiments, the data output by the image data input/output unit 501 maybe data extracted by the reference data extraction unit 502 and/or dataextracted by the target data extraction unit 503.

The reference data extraction unit 502 may extract reference data. Insome embodiments, the reference data may include one or more sets ofskeleton data. The skeleton data may refer to image data correspondingto a skeleton tissue. In some embodiments, the skeleton tissue mayinclude a skeleton of a lower limb, for example, a tibias, an ilia, asacrum, and/or a foot bone, etc. In some embodiments, the skeletontissue may include a skeleton tissue of a chest and/or an abdomen, forexample, vertebrae, and/or a rib, etc. In some embodiments, the skeletontissue may include a skeleton tissue of a head and/or a cervix, forexample, a skull, and/or neck vertebrae, etc. In some embodiments, thereference data extraction unit 502 may extract one or more sets ofskeleton data in the image data. In some embodiments, the reference dataextraction unit 502 may extract one or more sets of non-skeleton data(e.g., vessel data, fat data, data of other types of tissues) whenextracting skeleton data. For example, the data extracted by thereference data extraction unit 502 may include most of the skeleton dataand/or a small amount of vessel data, or data of other tissues in theimage data. In some embodiments, the reference data may include a firstreference data set. The first reference data set may include a targetdata set and/or a second reference data set. For example, the firstreference data set may include the skeleton data and the vessel data. Asanother example, the second reference data set may include the skeletondata.

The target data extraction unit 503 may extract target data. In someembodiments, the target data may include one or more data about one ormore vessels, that is, vessel data. The vessel data may refer to imagedata corresponding to the vessel(s). The vessel may be an artery vessel,a vein, etc. In some embodiments, the vessel may include a vessel of alower limb, for example, an iliac artery (or vein), a femoral artery (orvein), a popliteal artery (or vein), a tibial artery (or vein), afibular artery (or vein), an ankle artery (or veins), and/or a pedalartery (or vein), etc. In some embodiments, the vessel may include avessel of an abdomen, for example, an abdominal aorta (or vein), ahepatic artery (or vein), a splenic artery (or vein), and/or a renalartery (or vein), etc. In some embodiments, the vessel may include athoracic vessel, for example, an intercostal artery (or vein), apulmonary artery (or vein), aorta (or vein), a bronchial artery (orvein), an esophageal artery (or vein), and/or a gastric artery (orvein), etc. In some embodiments, the vessel may include a vessel of anupper limb, for example, an axillary artery (or vein), a brachial artery(or vein), a radial artery (or vein), and/or an ulnar artery (or vein),etc. In some embodiments, the vessel may include a vessel of a head andneck, for example, a left/right internal carotid artery (or vein), aleft/right external carotid artery (or vein), a left/right commoncarotid artery (or vein), a vertebral artery (or vein), an occipitalartery (or vein), a posterior auricular artery (or vein), a superficialtemporal artery (or vein), a lingual artery (or vein), an ophthalmicartery (or vein), a cerebral artery (or vein), etc. In some embodiments,the vessel data may include data relating to the vessel, for example,data of a centerline of the vessel, a boundary line of the vessel, or anendpoint of the vessel.

In some embodiments, the extraction of the target data may be performedbased on the data (e.g., the vessel data, the skeleton data, the fatdata, and/or other data of tissues) input by the image data input/outputunit 501, and/or the reference data (e.g., the skeleton data, the fatdata, and/or other data of tissues) extracted by the reference dataextraction unit 502. In some embodiments, the extraction of the targetdata may also be performed based on the frame data extracted by theframe data extraction unit 504. In some embodiments, the target dataextraction unit 503 may extract one or more other data when extractingthe target data. For example, if the target data are vessel data, otherdata may include the skeleton data, the fat data, or data of othertissues, etc.

The frame data extraction unit 504 may extract a frame data set. Theframe data set may include one or more target data. In some embodiments,the frame data set may include one or more target data that are easy tobe extracted. For example, if the target data are vessel data, the framedata set may include one or more data of vessels with relatively largediameters in the image data (e.g., an abdominal aorta, a tibial artery,and/or an iliac artery). As another example, if the target data arevessel data, the vessels near the skeleton are difficult to beextracted, and the frame data set may include data of vessels at aspecific distance from the skeleton.

FIG. 5-B is a flowchart illustrating an exemplary process for segmentinga vessel according to some embodiments of the present disclosure. Theprocess for segmenting a vessel may include obtaining image data 511,extracting reference data 512, and extracting target data 513.

In 511, image data may be obtained. The image data may be obtained fromthe data acquisition module 210, the storage module 220, the displaymodule 230, the input/out device 140, and/or the preprocessing submodule410, or obtained via the network 170. The image data input/output unit501 may perform 511.

In 512, reference data may be extracted. The reference data may beextracted by using one or more segmentation methods. The segmentationmethods may include a threshold method, a regional growth method, amethod based on energy function, a level set method, a regionalsegmentation and/or merging, an edge tracking segmentation method, astatistical pattern recognition method, a mean clustering segmentationmethod, a model method, a segmentation method based on deformable model,an artificial neuron network method, a minimum path segmentationpartition method, a tracking method, a rule-based segmentation method, acoupling surface segmentation method, or the like, or any combinationthereof. The reference data extraction unit 502 may perform 512.

In 513, target data may be extracted. The target data may be extractedby using one or more segmentation methods according to the presentdisclosure. In some embodiments, the extraction of the target data maybe performed based on the image data obtained in step 511 and thereference data extracted in 512. For example, the reference dataextracted in 512 may be removed from the image data obtained in step 511to obtain a result of removing the reference data, and the target datamay be extracted based on the result of removing the reference data. Thetarget data extraction unit 503 may perform 513.

In some embodiments, 514 may be added before 513. In 514, the frame dataset may be extracted. The extraction of the frame data set may beperformed based on the image data obtained in 511 and/or the referencedata extracted in 512. In some embodiments, the frame data may beextracted by using one or more segmentation methods according to thepresent disclosure. For example, the reference data may be removed fromthe image data to obtain the frame data set, and/or one or more imagesegmentation processes may be performed to obtain the frame data set.The frame data extraction unit 504 may perform 514. In 513, theextraction of the target data may be performed based on the frame dataset extracted in 514. For example, a regional growth may be performedbased on the frame data to obtain the target data.

It should be noted that the above descriptions about the vesselsegmentation submodule and the process for segmenting the vessel areprovided for illustration purposes, and should not be designated as theonly practical embodiment. Each unit may be implemented by one or morecomponents, and the function of each unit is not limiting. Each unit maybe added or omitted in specific scenarios. For persons having ordinaryskills in the art, after understanding the general principle of theprocess for segmenting the vessel, without departing the principle, maymodify or change the forms or details of the particular practical waysand steps, and further make simple deductions or substitutions, or maymake modifications or combinations of some steps without furthercreative efforts. For example, the image data input/output unit 501, theframe data extraction unit 504, the obtaining image data 511, and/or theextracting frame data 514 may be omitted. As another example, a datastorage operation may be added between 511, 512, 513, and/or 514, andthe data may be stored in the storage module 220. As yet anotherexample, one or more other image processing operations may be added to511, 512, 513, and/or 514, such as image dilation, image erosion, etc.

FIG. 6-A is a schematic diagram illustrating an exemplary vesselsegmentation submodule 420 according to some embodiments of the presentdisclosure. In some embodiments, the target data may be vessel data. Insome embodiments, according to the result of the reference dataextraction in 512 and the method of extracting the target data in 513,the target data extraction unit 503 may further include a broken vesselsegment extraction unit 603 and a vessel generation unit 604. In someembodiments, the vessel segmentation submodule 420 may include an imagedata input/output unit 601, a skeleton extraction unit 602, a brokenvessel segment extraction unit 603, and a vessel generation unit 604.

In some embodiments, the image data input/output unit 601 and the imagedata input/output unit 501 may include the same or similar structuresand functions; the skeleton extraction unit 602 and the reference dataextraction unit 502 may include the same or similar structures andfunctions, and the skeleton data may be extracted to provide thereference data.

The broken vessel segment extraction unit 603 may extract broken vesselsegments. The broken vessel segments may include a broken (or notconnected) segment of a vessel formed during the image segmentation. Insome embodiments, a vessel may include one or more broken vesselsegments. A broken vessel segment may include one or more data relatingto the vessel. Two or more broken vessel segments may include differentamounts of vessel data. In some embodiments, the broken vessel segmentextraction unit 603 may extract one, multiple, or all of the brokenvessel segments of a vessel.

The vessel generation unit 604 may generate vessels. The vesselgeneration may be performed based on the image data, the skeleton data,and/or the data of the broken vessel segments. In some embodiments, thevessel generation unit 604 may generate vessels between two or morebroken vessel segments based on the two or more broken vessel segments,and connect the two or more broken vessel segments by using one or moremethods to generate one or more vessels.

FIG. 6-B is a flowchart illustrating an exemplary process for segmentinga vessel according to some embodiments of the present disclosure. Insome embodiments, the process for vessel segmentation may furtherinclude obtaining original image data 611, extracting a skeleton 612,extracting broken vessel segments 613, and generating the vessel 614.

In 611, original image data may be obtained. 611 and 511 may be the sameor similar. For example, the original image data obtained in 611 may bethe same as the image data obtained in 511. The method for obtaining theoriginal image data in 611 may be the same as the method in 511. Theimage data input/output unit 601 may perform 611.

In 612, skeleton data may be extracted. The method for extracting theskeleton data may be the same as or similar to the method for extractingthe reference data in 512. In some embodiments, the extracted skeletondata may include one or more skeleton data. In some embodiments, theextracted skeleton data may also include one or more vessel data or dataof other tissues. In some embodiments, the extraction of the skeletondata may be performed for one or more times. The skeleton extractionunit 602 may perform 612.

In 613, broken vessel segments may be extracted. In some embodiments,the extraction of the broken vessel segments may be performed based onthe original image data obtained in 611 and/or the skeleton dataextracted in 612. For example, one or more skeleton data may be removedfrom the original image data to obtain data of the broken vesselsegments directly or indirectly. “Obtaining indirectly” may refer tothat the data of the broken vessel segments may be obtained after theskeleton data is removed and the result after the removal of theskeleton data is further processed. In some embodiments, one or moreoperations (e.g., image erosion, dilation, increase or decrease in thenumber of the skeleton data) may be performed based on the skeleton datato obtain the processed skeleton data. Further, the processed skeletondata may be removed from the original image data to obtain the data ofthe broken vessel segments directly or indirectly. In some embodiments,the data of the broken vessel segments may be extracted based on aresult of a first skeleton data extraction and a result of a secondskeleton data extraction. For example, if the result of the firstskeleton data extraction includes skeleton data and vessel data, and theresult of the second skeleton data extraction includes skeleton data,the result of the second skeleton data extraction may be removed fromthe result of the first skeleton data extraction to obtain the data ofthe broken vessel segments directly or indirectly. The broken vesselsegment extraction unit 603 may perform 613. For example, whenextracting a broken vessel segment near an ilium (e.g., a region withina spatial distance threshold from the ilium), the broken vessel segmentshould be selected near a y coordinate of the ilium's position (e.g., aregion within a distance threshold from the ilium in the y-direction),so that selection of crush bones near the vertebra may be avoided ordecreased.

In 614, vessels may be generated. The vessel generation may be performedbased on the data of the broken vessel segments extracted in 613. Insome embodiments, the vessels may be generated between two or morebroken vessel segments by using one or more algorithms. The vesselgeneration algorithm(s) may include one or more vessel segmentationtechniques described above or any combination thereof. The vesselgeneration unit 604 may perform 614.

FIG. 7-A is a schematic diagram illustrating an exemplary skeletonextraction unit 602 according to some embodiments of the presentdisclosure. The skeleton extraction unit 602 may include askeleton-vessel segmentation sub-unit 701, a boundary distance fieldcalculation sub-unit 702, a vessel segmentation sub-unit 703, and askeleton segmentation sub-unit 705.

The skeleton-vessel segmentation sub-unit 701 may segment askeleton-vessel to obtain skeleton-vessel data. The skeleton-vessel datamay include one or more skeleton data and one or more vessel data. Insome embodiments, the skeleton-vessel data may include at least part ofskeleton data, at least part of vessel data, and/or data of othertissues in the image data. For example, for image data of the lowerlimb, the skeleton-vessel data may include ilium data, femur data, tibiadata, fibula data, and/or foot bone data, or the like, or a combinationthereof; the skeleton-vessel data may also include data of an iliacartery, a femoral artery, a popliteal artery, a tibial artery, a fibularartery, an ankle artery, and/or a pedal artery, or the like, or acombination.

The boundary distance field calculation sub-unit 702 may calculate aboundary distance field of the image data and/or the skeleton-vesseldata. The boundary distance field may include boundary distance value(s)of one or more pixels (or voxels) of the image. The boundary distancevalue(s) of the one or more pixels (or voxels) of the image may form adistance field. In some embodiments, the boundary distance value mayrefer to a distance from a pixel (or voxel) to a boundary. The boundarymay be one or more pixels (or voxels) designated manually orautomatically. For example, the boundary may refer to a boundary of askeleton or a boundary of a vessel, etc.

The vessel segmentation sub-unit 703 may segment a vessel. The vesselsegmentation may be performed based on the image data, theskeleton-vessel data, and/or the boundary distance field data. In someembodiments, the vessel segmentation sub-unit 703 may perform vesselsegmentation on the boundary distance field of the skeleton-vessel data.

The skeleton segmentation sub-unit 704 may segment a skeleton. Theskeleton segmentation may be performed based on the image data, theskeleton-vessel data, the boundary distance field data, and/or thevessel segmentation data. In some embodiments, the skeleton segmentationsub-unit 704 may perform skeleton segmentation on the boundary distancefield of the skeleton-vessel data and the vessel segmentation data.

FIG. 7-B is a flowchart illustrating an exemplary process for extractinga skeleton according to some embodiments of the present disclosure. Theprocess for extracting a skeleton may include obtaining a first image711, obtaining a vessel mask 712, obtaining a first subtraction image714, obtaining a first skeleton mask 715, and obtaining a secondskeleton mask 716.

In 711, a first image may be obtained. The first image may includepixels (or voxels) corresponding to the skeleton-vessel data. In someembodiments, the first image may include one or more two-dimensional (orthree-dimensional) connected domains of most of the vessels and the bonetissues. In some embodiments, the pixels (or voxels) in the first imageand the data in the first reference data set in FIG. 5-A may bebijective. Obtaining of the first image may be performed based on theoriginal image data obtained in 611. In some embodiments, the firstimage obtained in 711 may include at least part of the skeleton data andat least part of the vessel data in the image data. The first image maybe obtained by using the one or more segmentation methods describedabove. For example, the first image may be obtained by using a thresholdmethod. Specifically, a threshold T₁ may be set, and the data largerthan the threshold T₁ may be extracted as skeleton-vessel data. In someembodiments, the threshold T₁ may be automatically, semi-automatically,or manually set. For example, the threshold may be automaticallycalculated or selected based on one or more operations. As anotherexample, a user or operator may manually determine the threshold througha graphical user interface in the input/out device 140. As yet anotherexample, a user or operator may, according to the automatically selectedthreshold, manually modify, change, or the like. Specifically, if theimage data are CT values, CT values of the vessel may be relativelyfixed, for example, in a range of 100 to 800 Hounsfield units (HU);while a CT value range of a skeleton may be relatively wide, someskeletal CT values may be smaller than the CT values of a vessel, andsome skeletal CT values may be larger than the CT values of a vessel.Therefore, in some embodiments, a value smaller than the minimum CTvalue of the vessel may be selected as the threshold. Theskeleton-vessel segmentation sub-unit 701 may perform 711.

In 712, a vessel mask may be obtained. In some embodiments, the vesselmask may be obtained through vessel segmentation. The vesselsegmentation may be performed by using one or more image segmentationmethods described above. For example, the vessel may be segmented byusing a regional growth method. As another example, the vessel may besegmented by using a threshold method. A threshold T₂ may beautomatically, semi-automatically, or manually set. For example, thethreshold T₂ may be automatically set. As another example, a user oroperator may manually determine the threshold through a graphical userinterface in the input/out device 140. As another example, a user oroperator may, according to the automatically selected threshold,manually modify, change, or the like. For example, the threshold T₂ maybe determined as 2.5 (or 2.9) (a distance value). It should be notedthat the values of the threshold T₂ listed above are for illustrationpurposes and are by way of example only and are not intended to limitthe scope of the present disclosure as set forth. In some embodiments,the result of the vessel segmentation may be directly recorded as avessel mask and used for subsequent operations. In some examples, thesegmented vessel may be further processed, the result after processingmay be recorded as a vessel mask and used for subsequent operations. Insome embodiments, the operations may include a dilation operation or anerosion operation on the vessel (as shown in FIG. 7-E). The vesselsegmentation sub-unit 703 may perform 712.

In 714, the vessel mask obtained in 712 may be subtracted from the firstimage to obtain a first subtraction image. In some embodiments, thefirst subtraction image may include a primary part of a skeleton tissueand a small number of small vessels, and a primary part of a vesselregion (e.g., the vessel mask obtained in 712) may be subtracted througha subtraction operation. In some embodiments, the subtraction operationin 714 may refer to removing the pixels (or voxels) corresponding to thepixels (or voxels) in the vessel mask from the first image, andrecording the remaining pixels (or voxels) as the first subtractionimage. In some embodiments, the subtraction operation in 714 may referto subtracting the values of the pixels (or voxels) in the first imagefrom values of the corresponding pixels (or voxels) in the vessel maskand recording the subtracted results as the first subtraction image. Theskeleton segmentation sub-unit 704 may perform 714.

In 715, a first skeleton mask may be obtained. In some embodiments, thefirst skeleton mask may be obtained by segmenting a skeleton. Theskeleton segmentation may be performed based on the first subtractionimage obtained in 714. The skeleton segmentation may be performed byusing one or more image segmentation methods described above. Forexample, the skeleton may be segmented by using a threshold method inthe first subtraction image. A threshold T₃ may be automatically,semi-automatically, or manually set. For example, the threshold T₃ canbe automatically set by an operation of the data in the firstsubtraction image. As another example, a user or operator may manuallydetermine the threshold through a graphical user interface in theinput/out device 140. As another example, a user or operator may,according to the automatically selected threshold, manually modify,change, or the like. In some embodiments, the threshold T₃ may bedetermined as 1.5 (a distance value). In some embodiments, one or moreseed points may be selected for the skeleton segmentation based on thethreshold T₃. For example, a pixel (or voxel) including a distance valuelarger than 3.0 may be selected as the seed point to segment a skeleton.It should be noted that the value of the threshold T₃ and the distancevalue of the seed point described above are for illustration purposesand are by way of example only and are not intended to limit the scopeof the present disclosure as set forth. In some embodiments, the resultof the skeleton segmentation may be recorded as the first skeleton maskand used for subsequent operations. The skeleton segmentation sub-unit704 may perform 715.

In 716, the first skeleton mask may be further processed to obtain asecond skeleton mask. In some embodiments, the pixels (or voxels) in thefirst skeleton mask or the second skeleton mask and the data in thesecond reference data set in FIG. 5-A may be bijective. In someembodiments, through the process in 716, a part of a vessel that ismis-segmented in the first skeleton mask may be removed. For example,one or more pixels (or voxels) in the one or more vessels correspondingto the first skeleton mask may be recorded in the first skeleton maskand are mistaken for skeleton pixels (or voxels). In some embodiments,processing methods may include a morphological processing method, forexample, dilation, erosion operations, or the like of the skeleton. Insome embodiments, the process may be performed within a range of pixels(or voxels) in the first skeleton mask. For example, a dilationoperation may be performed within a range of the pixels (or voxels) inthe first skeleton mask. The dilation operation may refer to the use ofa structural element including a certain size, moving it in the pixel(or voxel) of the image where the skeleton may be located, performing an“AND” operation of the structural element with the image it overlaps,and if the result of the “AND” operation is 0, pixel value(s) of theresulting image may be 0; otherwise 1. In some embodiments, through thedilation operation, the region where the skeleton may be located may belarger than the region before the operation. The skeleton segmentationsub-unit 704 may perform 716.

It should be noted that the above descriptions about the skeletonextraction unit and the process for extracting the skeleton are providedfor illustration purposes, and should not be designated as the onlypractical embodiment. Each sub-unit may be implemented by one or morecomponents, and the function of each sub-unit is not limiting. Eachsub-unit may be added or omitted in specific scenarios. For personshaving ordinary skills in the art, after understanding the generalprinciple of the process for extracting the skeleton without departingthe principle, may modify or change the forms or details of theparticular practical ways and steps, and further make simple deductionsor substitutions, or may make modifications or combinations of somesteps without further creative efforts. For example, an imagetransformation sub-unit may be added to transform the image. As anotherexample, steps 715 and 716 may be combined into one operation. A datastorage operation may be added among 711 to 716 or thereafter. The datamay be stored in the storage module 220.

FIG. 7-C is a flowchart illustrating an exemplary process for obtaininga first image according to some embodiments of the present disclosure.The process for obtaining a first image may include determining aconnected domain in an original image 721, determining a seed point 722,and obtaining a first image 723. In some embodiments, the first imagemay be obtained by the skeleton-vessel segmentation sub-unit 701.

In 721, one or more connected domains in the original image may bedetermined. The original image may refer to an image corresponding tothe original image data obtained in 611. In some embodiments, theoriginal image may be a slice image (i.e., layer image) in athree-dimensional image. In some embodiments, the skeleton-vesselsegmentation sub-unit 701 may determine one or more connected domainsbased on gray scale information. For example, the skeleton-vesselsegmentation sub-unit 701 may determine pixels (or voxels) as aconnected domain. Gray-scale values of the pixels (or voxels) may bewithin a predetermined range and/or the pixels (or voxels) may includeadjacency relation.

In 722, a seed point may be determined. In some embodiments, theskeleton-vessel segmentation sub-unit 701 may determine the seed pointbased on a circularity degree of the connected domain. In someembodiments, the seed point may include a skeleton seed point and/or avessel seed point. For example, the abdominal aorta may be approximatelycylindrical in a three-dimensional space; the abdominal aorta may berepresented by a round section in the slice image (i.e., layer image);the skeleton-vessel segmentation sub-unit 701 may determine a connecteddomain in the original image, and the connected domain may include around section representing the abdominal aorta, and/or other irregularconnected domains; the round section representing the abdominal aortamay be determined by comparing the circularity degrees of differentconnected domains for determining the seed point. In some embodiments,the circularity degree of the connected domain may refer to the ratio ofthe circle area S of the connected domain to the actual area S′ of theconnected domain. The circle area S of the connected domain may refer tothe area of a circular region including a shape similar to the connecteddomain. In some embodiments, the skeleton seed point and the vessel seedpoint may be determined according to similar methods. FIG. 7-D showsexamples of methods for determining a seed point.

In 723, a regional growth may be performed on the original image basedon the seed point determined in 722 to obtain the first image. The firstimage may include one or more two-dimensional (or three-dimensional)connected domains including a large amount of vessels and skeletons. Insome embodiments, the skeleton-vessel segmentation sub-unit 701 mayperform the regional growth on the original image based on a thresholda. In some embodiments, if the original image is a three-dimensionalimage, in 723, the skeleton-vessel segmentation sub-unit 701 may performa three-dimensional regional growth based on the seed point, and theobtained first image may include one or more three-dimensional connecteddomains. In some embodiments, the threshold a may be a threshold of agray-scale value, for example, a gray-scale value of 200.

FIG. 7-D is a flowchart illustrating an exemplary process fordetermining a seed point according to some embodiments of the presentdisclosure. The process for determining a seed point may includedetermining a value of a boundary distance field 731, determining aradius of the connected domain 732, determining a circle area of theconnected domain 733, determining a circularity degree 734, anddetermining a seed point 735. In some embodiments, the boundary distancefield calculation sub-unit 702 may perform 731. In some embodiments, thedetermining the seed point may be performed by the skeleton-vesselsegmentation sub-unit 701.

In 731, a value of a boundary distance field of one or more pixels inany of the connected domains in the above-mentioned slice image may bedetermined. The value of the boundary distance field may refer to adistance between the pixel and the nearest boundary point. In someembodiments, a boundary point may be a pixel in the boundary of theslice image. In some embodiments, the boundary point may be a pixel inthe boundary of the connected domain. In some embodiments, the distancemay be expressed as the number of pixels (i.e., a pixel width) betweenthe pixel and the nearest boundary point. In some embodiments, thedistance may be calculated by using a distance field formula asrepresented by Equation (1):

d ₂=√{square root over ((x−i)²+(y−j)²)}  (1),

wherein d₂ may refer to the value of the two-dimensional boundarydistance field of the pixel, (x, y) is the coordinate of the pixel inthe connected domain, and (i, j) is the coordinate of the boundary pointclosest to the pixel. In some embodiments, values of the distance fieldof one or more pixels in the connected domain may be collected togenerate a data set pfield-1. In some embodiments, this may mean thatthe smaller the value of the boundary distance field is, the closer thepixel is to the boundary point; the larger the value of the boundarydistance field is, the farther the pixel is from the boundary point.

In 732, the radius r of the connected domain may be determined based onthe data set pfield-1. In some embodiments, the skeleton-vesselsegmentation sub-unit 701 may determine the radius r of the connecteddomain according to one or more standards. The standards may include amaximum value of the boundary distance, an average value, a median, anda modal number of values of two or more boundary distance, or the like.For example, the maximum value of the boundary distance field may beselected from the data set pfield-1 as the radius r of the connecteddomain.

In 733, the circle area S of the connected domain may be determinedbased on the radius r of the connected domain. In some embodiments, thecircle area S may be determined based on the circle area formula S=πr².

In 734, the circularity degree c of the connected domain may bedetermined based on the circle area S and the actual area S′ of theconnected domain. In some embodiments, the circularity degree c may bedetermined based on the ratio of the circle area S and the actual areaS′ of the connected domain; that is, c=S/S′. In some embodiments, thismay mean that the closer the circularity degree c is to 1, the closerthe shape of the connected domain is to a circle. In some embodiments,the circularity degree(s) of one or more connected domains in the sliceimage may be determined based on the methods from 731 to 734. Thecircularity degrees may be collected to generate a data set dataset-1.

In 735, a seed point may be determined. In some embodiments, theskeleton-vessel segmentation sub-unit 701 may select one or more targetconnected domains based on the data set dataset-1. In some embodiments,the target connected domain may be a connected domain including themaximum circularity degree, or including a circularity degree closeto 1. In some embodiments, the target connected domain may beautomatically, semi-automatically, or manually determined. For example,in some embodiments, the skeleton-vessel segmentation sub-unit 701 maycalculate absolute values of the difference values between all thecircularity degrees and 1 in the data set dataset-1, and determine aconnected domain with the minimum absolute value or a small absolutevalue as the target connected domain. As another example, a user oroperator may completely manually determine a target connected domainthrough a graphical user interface in the input/out device 140.Alternatively, possible connected domains that are already determined(e.g., determined by the skeleton-vessel segmentation sub-unit 701) maybe presented to a user or operator, and then the user or operator mayselect a target connected domain through a graphical user interface inthe input/out device 140. This process of selecting a target connecteddomain may be called semi-automatic selection. As another example, auser or operator may, according to the automatically selected targetconnected domain, manually modify, change, or the like.

In some embodiments, the skeleton-vessel segmentation sub-unit 701 maydetermine one or more pixels in the target connected domain as the seedpoint(s). In some embodiments, the seed points may be automatically,semi-automatically, or manually determined. In some embodiments, theskeleton-vessel segmentation sub-unit 701 may automatically determine aseed point according to one or more standards, for example, determininga pixel including the maximum value or a large value of the boundarydistance field in the target connected domain as the seed point. Asanother example, a user or operator may completely manually determinethe seed point through a graphical user interface in the input/outdevice 140. Alternatively, the one or more possible seed points that arealready determined (e.g., determined by the skeleton-vessel segmentationsub-unit 701) may be presented to a user or operator, and then the useror operator may select a target seed point through a graphical userinterface in the input/out device 140. This process of determining theseed point may be called semi-automatic determination. As anotherexample, a user or operator may, according to the automatically selectedthreshold, manually modify, change, or the like. In some embodiments,731 to 735 may be performed in the slice image (i.e., layer image ortwo-dimensional image), the seed point determined in 735 may be a pixelin the two-dimensional image, and the seed point may correspond to avoxel in the three-dimensional image.

FIG. 7-E is a flowchart illustrating an exemplary process for obtaininga vessel mask according to some embodiments of the present disclosure.The process for obtaining a vessel mask may include determining a valueof a boundary distance field 741, obtaining a first vessel mask 742, andobtaining a second vessel mask 743. In some embodiments, the boundarydistance field calculation sub-unit 702 may perform 741. In someembodiments, the first vessel mask and the second vessel mask may beobtained by the vessel segmentation sub-unit 703.

In 741, values of the boundary distance field of the pixels/voxels inthe first image may be determined. The values of the boundary distancefield may be collected to generate a data set pfield-2. In someembodiments, a value of the boundary distance field may refer to adistance from a pixel/voxel to the nearest boundary point. In someembodiments, the boundary point may be a pixel/voxel in the boundary ofthe first image. In some embodiments, the distance may be expressed asthe number of pixels/voxels (i.e., pixel/voxel width) between thepixel/voxel and the nearest boundary point. In some embodiments, as fora two-dimensional pixel, the distance may be calculated by usingEquation (1). In some embodiments, for a three-dimensional voxel, thedistance may be calculated by a distance field formula as represented byEquation (2):

d ₃=√{square root over ((x−i)²+(y−j)²+(z−k)²)}  (2),

wherein d₃ may refer to the value of the three-dimensional boundarydistance field of the pixel, (x, y, z) may refer to the coordinate ofone voxel in the first image, and (i, j, k) may refer to the coordinateof the boundary point closest to the voxel. In some embodiments, thismay mean that the smaller the value of the boundary distance field is,the closer the pixel/voxel is to the boundary point; the larger thevalue of the boundary distance field is, the farther the pixel/voxel isfrom the boundary point. In some embodiments, the boundary distancefield values of all the pixels/voxels in the first image may form animage of a boundary distance field. The pixels/voxels in the image ofthe boundary distance field and the pixels/voxels in the first image maybe bijective.

In 742, a first vessel mask may be obtained. In some embodiments, theskeleton-vessel segmentation sub-unit 701 may perform a two-dimensionalregional growth or a three-dimensional regional growth on the firstimage based on a seed point. In some embodiments, the seed point may bethe seed point determined in 722 (e.g., the seed point determined in735). In some embodiments, the operation of regional growth may beperformed based on a threshold. In some embodiments, the regional growthmay be performed in the image of the boundary distance field. Thethreshold may be determined based on the values of the boundary distancefield, for example, a value of the boundary distance field larger than3.0 as the threshold. In some embodiments, the radius of the vessel inthe first image may decrease and be closer to the bone along the thevessel. This may mean that the closer the vessel is to a boundary pointof the first image, the closer the vessel is to the bone, the smallerthe vessel radius is, and the smaller the value of the boundary distancefield of the pixel/voxel is. Therefore, the following may be controlledby determining the threshold: the vessel does not grow to the skeleton,and a vessel with a small radius does not grow. The first vessel maskmay be obtained after performing the regional growth on the first image.The first vessel mask may include pixels/voxels corresponding tovascular tissues including large radii.

In 743, a dilation operation may be performed on the first vessel maskto obtain a second vessel mask. In some embodiments, the vesselsegmentation sub-unit 703 may perform the dilation operation by using astructural element. The structural element may include a certain size,for example, the width of 4 pixels/voxels. The second vessel mask may beobtained after the dilation operation. The second vessel mask mayinclude pixels/voxels representing the vascular tissue. Compared to thefirst vessel mask, the second vessel mask may include more vasculartissues. In some embodiments, the second vessel mask may include most ofthe vascular tissue in the first image.

FIG. 7-F is a flowchart illustrating an exemplary process for obtaininga first skeleton mask according to some embodiments of the presentdisclosure. The process for obtaining the first skeleton mask mayinclude determining a value of the boundary distance field 751,determining whether the value of the boundary distance field exceeds athreshold 752, excluding a pixel/voxel 753, determining a skeleton seedpoint 754, determining a threshold 755, and obtaining a first skeletonmask 756. In some embodiments, the boundary distance field calculationsub-unit 702 may perform 751. In some embodiments, the first skeletonmask may be obtained by the skeleton segmentation sub-unit 704.

In 751, values of the boundary distance field of the pixels/voxels inthe first subtraction image may be determined. The values of theboundary distance field values may be collected to generate a data setpfield-3. In some embodiments, for a two-dimensional pixel, the value ofthe boundary distance field may be calculated by using Equation (1). Insome embodiments, for a three-dimensional voxel, the value of theboundary distance field may be calculated by using Equation (2).

In 752, whether the value of the boundary distance field exceeds athreshold b may be determined. In some embodiments, the threshold b maybe a value of the boundary distance field larger than 3.0. In someembodiments, the threshold b may be a default threshold of the dataprocessing system 130 or a threshold set based on the result of theskeleton segmentation, or a threshold set by a user or operator througha graphical user interface in the input/out device 140.

In 753, the pixel/voxel including a value of the boundary distance fieldsmaller than the threshold b may be excluded. This may mean that thepixel/voxel including a value of the boundary distance field smallerthan the threshold b may not be determined as the skeleton seed point.

In 754, a skeleton seed point may be determined. In some embodiments,the pixel/voxel including a value of the boundary distance field largerthan the threshold b may be determined as the skeleton seed point. Sincethe vessel, the mask has been subtracted from the first subtractionimage and the vessel mask may include the pixels/voxels with largevalues of the boundary distance field that may represent the vasculartissues, the pixels/voxels having large values of the boundary distancefield in the first subtraction image may represent skeleton tissue.

In 755, a threshold b′ may be determined. In some embodiments, thethreshold b′ may refer to the threshold used in the regional growth in756. In some embodiments, the threshold b′ may include a threshold ofgray scale information and/or a threshold of the boundary distancefield. In some embodiments, the gray scale information may include atexture structure, a gray-scale value, an average grayscale, intensity,a color saturation, contrast, brightness, or the like, or a combinationthereof. For example, the threshold b′ may be a gray-scale value largerthan 500, or a value of a boundary distance field greater than 1.5. Insome embodiments, the threshold may be automatically,semi-automatically, or manually determined. For example, the skeletonsegmentation sub-unit 704 may automatically determine the threshold b′based on one or more standards. As another example, a user or operatormay completely manually input the threshold b′ through a graphical userinterface in the input/out device 140. Alternatively, one or morepossible thresholds that are already determined (e.g., determined by theskeleton segmentation sub-unit 704) may be presented to a user oroperator, and then the user or operator may select a threshold b′through a graphical user interface in the input/out device 140. Thisprocess of determining the threshold may be called semi-automaticdetermination. As yet another example, a user or operator may, accordingto the automatically selected threshold b′, manually modify, change, orthe like.

In 756, the regional growth may be performed based on the skeleton seedpoint determined in 754 and the threshold b′ determined in 755 to obtaina first skeleton mask. In some embodiments, the regional growth may beperformed based on the gray scale information of the first subtractionimage, or based on the values of the boundary distance field of thepixels/voxels in the first subtraction image. Accordingly, the thresholdb′ may be a threshold of gray scale information or a threshold of thevalue of the boundary distance field. The obtained first skeleton maskmay include pixels/voxels representing skeletons, and a small amount ofother pixels/voxels representing non-skeletons (e.g., vessels).

FIG. 7-G is a flowchart illustrating an exemplary process for obtaininga second skeleton mask according to some embodiments of the presentdisclosure. The process for obtaining the second skeleton mask mayinclude determining a connected domain 761, determining a skeletonregion 762, obtaining a first temporary skeleton mask 763, obtaining asecond image 764, filling a second image 765, obtaining asuperimposition image 766, and obtaining a second skeleton mask 767. Insome embodiments, the boundary distance field calculation sub-unit 702may perform 761. In some embodiments, the second skeleton mask may beobtained by the skeleton segmentation sub-unit 704.

In 761, the erosion operation may be performed on the first skeletonmask to determine a connected domain. The erosion operation may refer tothe use of a structural element including a certain size, moving it inthe pixel (or voxel) of the image where the skeleton may be located,performing an “AND” operation of the structural element with the imageit overlaps; and if the result of the “AND” operation is 1, pixel valuesof result image may be 1, otherwise 0. Since the first skeleton mask mayinclude pixels/voxels representing a vessel, one or more pixels/voxelsrepresenting the vessel may be removed through the erosion operation. Insome embodiments, the pixels/voxels representing the vessel in the firstskeleton mask may be close to the pixels/voxels representing theskeleton and may be difficult to distinguish from each other, andconnection part of the vessel, and the skeleton may be broken throughthe erosion operation. In some embodiments, one or more pixels/voxelsrepresenting the skeleton may be missing through the erosion operation.In some embodiments, the skeleton segmentation sub-unit 704 may performthe erosion operation by using a structural element. The structuralelement may include a certain size, for example, a width of 5pixels/voxels. In some embodiments, one or more connected domains may beobtained after the erosion operation.

In 762, a skeleton region may be determined. In some embodiments, theskeleton segmentation sub-unit 704 may determine one or more skeletonregions based on the connected domain determined in 761. In someembodiments, the skeleton region may be automatically,semi-automatically, or manually determined. For example, the skeletonregion may be automatically determined according to one or morestandards. The standards may include selecting a connected domainincluding a maximum area or a maximum volume. As another example, a useror operator may manually determine a skeleton region through a graphicaluser interface in the input/out device 140. Alternatively, one or morepossible skeleton regions that are already determined (e.g., determinedby the skeleton segmentation sub-unit 704) may be presented to a user oroperator, and the user or operator may select a skeleton region througha graphical user interface in the input/out device 140. This process ofdetermining the skeleton region may be called semi-automaticdetermination. As yet another example, a user or operator may, accordingto the automatically selected skeleton region, manually modify, change,or the like.

In 763, the dilation operation may be performed on the skeleton regionto obtain a first temporary skeleton mask. In some embodiments, theskeleton segmentation sub-unit 704 may perform the dilation operation byusing a structural element. The structural element may include a certainsize, for example, a width of 5 pixels/voxels. By the dilationoperation, the obtained first temporary skeleton mask may restore one ormore pixels/voxels representing the skeleton that are missing due to theerosion process in 761. In some embodiments, the first temporaryskeleton mask may include pixels/voxels representing the skeleton, whilepixels/voxels representing the vessel may be removed.

In 764, a second image may be obtained. In some embodiments, theregional growth may be performed on the original image based on one ormore seed points and a threshold e to obtain the second image. In someembodiments, the seed point may be the seed point determined in 722. Insome embodiments, the threshold e may be smaller than the threshold aused in 723. In some embodiments, since grayscale information of theskeleton is below the skeleton tissue, etc., the threshold a used in 723may result in missing of part of skeleton information in the firstimage. Using a threshold e smaller than the threshold a to perform theregional growth may supplement the missing skeleton information.Compared to the first image, the second image may include more skeletoninformation.

In 765, the second image may be filled to obtain a filled second image.Filling the second image may refer to filling the connected domainrepresenting the skeleton region in the second image. In someembodiments, the skeleton segmentation sub-unit 704 may fill theconnected domain in the second image layer by layer. For example, theskeleton segmentation sub-unit 704 may select a slice image (i.e., layerimage) of the second image to perform the regional growth on backgroundregion based on the result of the first temporary skeleton mask toobtain a binary image of the background region (e.g., the gray-scalevalue of the background region may be 1, the gray-scale value of theconnected domain in the skeleton region may be 0, as shown in FIG. 7-H).Then, the skeleton segmentation sub-unit 704 may perform a reverseoperation on the binary image, for example, determining the gray-scalevalue of the background region as 0, determining the gray-scale value ofthe connected domain in the skeleton region as 1, so that filling of theskeleton region in the slice image may be performed (as shown in FIG.7-H). Similarly, the filling operation may be performed on the secondimage layer by layer to obtain the filled second image. An example offilling the skeleton region is shown in FIG. 7-H.

In 766, a superimposition image may be obtained based on the firsttemporary skeleton mask and the filled second image. In someembodiments, the skeleton segmentation sub-unit 704 may performsuperimposition of the first temporary skeleton mask and the filledsecond image. In some embodiments, the superimposition may refer tosuperimposition of the first temporary skeleton mask and thecorresponding connected domain in the filled second image. In someembodiments, the superimposition operation may refer to adding up thevalues of corresponding pixels/voxels. In some embodiments, possiblevoids or missing skeleton edges may be filled through thesuperimposition operation. In some embodiments, great gaps or voids inthe skeleton region may be filled through the superimposition operation.In some embodiments, there may be several gaps or voids with smallpixel/voxel widths between the connected domains in the superimpositionimage, and the skeleton segmentation sub-unit 704 may further perform aclosing operation on the superimposition image. The closing operationmay refer to performing dilation on the superimposition image by using afirst structural element and then performing erosion on dilatedsuperimposition image by using a second structural element. The firststructural element and the second structural element may include thesame size or different sizes.

In 767, the closing operation may be performed in the connected domainincluding a maximum volume of the superimposition image to obtain asecond skeleton mask. In some embodiments, the skeleton segmentationsub-unit 704 may extract the connected domain including the maximum areaor the maximum volume in the superimposition image. In some embodiments,the connected domain including the maximum volume may correspond to askeleton region. In the skeleton region, a mis-extracted vessel may bealready removed, and one or more pixels/voxels representing the skeletonmay also be removed. The closing operation may refer to performingdilation on the superimposition image by using a first structuralelement and then performing erosion on dilated superimposition image byusing a second structural element. The first structural element and thesecond structural element may include the same size or different sizes(e.g., a width of 10 pixels/voxels). In some embodiments, gaps or voidsincluding small sizes (e.g., a width of several pixels/voxels) in theskeleton region may be filled through the closing operation.

FIG. 7-H is a schematic diagram illustrating an exemplary fillingoperation on a skeleton region according to some embodiments of thepresent disclosure. An image 777 may represent a binary image of a sliceimage. The gray values of the edges of the skeleton regions 771 a and771 b may be 1, the skeleton regions 771 a and 771 b may include hollowstructures, and the gray-scale value of a background region 772 is 0. Animage 778 may represent a binary image of an intermediate result of thefilling operation. In the image 778, the regional growth may beperformed on the background region 774. For example, a seed point may beselected in the edges of the skeleton regions 771 a or 771 b of theimage 777, and then the regional growth may be performed on thebackground region 774. The gray-scale value of the background region 774may be 1, and the gray-scale value of a connected domain where theskeleton regions 773 a and 773 b are located may be 0. The image 779 mayrepresent a binary image of the result of the filling operation. Theimage 779 may be obtained by performing the reverse operation on theimage 778, for example, the pixels/voxels including a gray-scale valueof 1 is modified to include a gray-scale value of 0, and thepixels/voxels including a gray-scale value of 0 is modified to include agray-scale value of 1. The skeleton regions 775 a and 775 b in the image779 have been filled through such processes.

FIG. 8 is a flowchart illustrating an exemplary process for generating avessel according to some embodiments of the present disclosure. Theprocess for generating the vessel may include obtaining a secondsubtraction image 811 and generating a vessel 812.

In 811, a second subtraction image may be obtained. In some embodiments,the second subtraction image and the data of the broken vessel segmentdescribed in FIG. 6-B may be bijective. In some embodiments, the secondsubtraction image and the data in the frame data set of FIG. 5-B may bebijective. In some embodiments, the second subtraction image may includeone or more connected domains. The connected domain may include brokenvessel segments and/or skeleton fragments. In some embodiments, thebroken vessel segment extraction unit 603 may perform 811. In someembodiments, the broken vessel segment extraction unit 603 may subtractthe second skeleton mask from the first image to obtain a secondsubtraction image. In some embodiments, the subtraction operation mayrefer to removing pixels/voxels corresponding to the second skeletonmask from the first image, and the remaining pixels/voxels in the firstimage are recorded as a second subtraction image. In some embodiments,values of the pixels/voxels in the first image may be different fromvalues of the corresponding pixels/voxels in the second skeleton mask.The subtraction operation may refer to subtracting the values of thecorresponding pixels/voxels in the second skeleton mask from the valuesof the pixels/voxels in the first image, and the obtained values of thepixels/voxels may be recorded as a second subtraction image. In someembodiments, since the methods, processes, or parameters used aredifferent, the obtained data in the broken vessel segment may includeone or more data of skeleton fragments. The skeleton fragments may referto data of residual skeleton tissues produced at the edge of the vesseldue to the skeleton segmentation during processing.

In 812, a vessel may be generated based on a second subtraction image.In some embodiments, the vessel generation unit 604 may perform 812. Insome embodiments, the vessel may be generated between two or more brokenvessel segments (e.g., the connected domain obtained in 811) by usingone or more algorithms. The vessel generation algorithms may include oneor more vessel segmentation methods described above and any combinationthereof. The detailed description of the vessel generation is as shownin FIGS. 10-A to 10-E.

FIG. 9 is a schematic diagram illustrating an exemplary vesselgeneration unit 604 according to some embodiments of skeleton thepresent disclosure. In some embodiments, the vessel generation unit 604may further include a first vessel generation sub-unit 901, a secondvessel generation sub-unit 902, a third vessel generation sub-unit 903,and a fourth vessel generation sub-unit 904. The first vessel generationsub-unit 901 may generate a vessel based on a site where a vessel of atibia may break. The detailed process for generating the vessel of thetibia is shown in FIG. 10-A. The second vessel generation sub-unit 902may generate a vessel based on a site where a vessel of a foot maybreak. The detailed flow of generating the vessel of the foot is shownin FIG. 10-B. The third vessel generation sub-unit 903 may generate avessel based on a site where a vessel of an ilium may break. Thedetailed process for generating the vessel of the ilium is shown in FIG.10-D. The fourth vessel generation sub-unit 904 may generate acorresponding vessel based on a site where the corresponding vessel maybreak. The detailed process for generating the vessel is shown in FIG.10-E. In some embodiments, the fourth vessel generation sub-unit 904 maygenerate a vessel of a tibia, a vessel of a foot, a vessel of an ilium,and/or any other vessel other than the vessel of the tibia, the vesselof the foot, and the vessel of the ilium.

It should be noted that the above descriptions about the vesselgeneration module are provided for illustration purposes, and should notbe designated as the only practical embodiment. Each submodule may beimplemented by one or more components, and the function of eachsubmodule is not limiting. Each submodule may be added or omitted inspecific scenarios. For persons having ordinary skills in the art, afterunderstanding the general principle of the process for generating thevessel, without departing the principle, may modify or change the formsor details of the particular practical ways and steps, and further makesimple deductions or substitutions, or may make modifications orcombinations of some steps without further creative efforts. Forexample, the first vessel generation sub-unit 901, the second vesselgeneration sub-unit 902, the third vessel generation sub-unit 903,and/or the fourth vessel generation sub-unit 904 may be combined intoone unit.

FIG. 10-A is a flowchart illustrating an exemplary process forsupplementing tibia vessel data according to some embodiments of thepresent disclosure. Supplementing the tibia vessel data may be performedby the first vessel generation sub-unit 901. In 1001, data missingpoints representing the vessel of the tibia between the broken vesselsegments may be identified based on vessel broken data. For example, thedata missing points of the vessels may be a site between the brokenvessel segments in the image data (e.g., the image data of a crus siteobtained in 611), including but not limited to a vessel segment thatshould be connected and false positive data.

In 1002, the first vessel generation sub-unit 901 may determine datasupplement regions based on the data missing points representing thevessel of the tibia obtained in 1001. In some embodiments, the datasupplement region may refer to a vessel segment that is blocked by theskeleton and should have been connected. In some embodiments, the firstvessel generation sub-unit 901 may extract a bone section of every layerto form a histogram, based on the skeleton data (for example, theskeleton data extracted by the skeleton extraction unit 602). In someembodiments, the horizontal axis of the histogram may represent thenumber of layers, and the longitudinal axis of the histogram mayrepresent the size of the bone section. In some embodiments, the numberof layers may be determined along the direction perpendicular to thehorizontal axis. Along the direction of the knee to foot, a positionwhere the size of the bone section nearest to the foot in the histogramis maximum may be considered as an approximate position where the vesselof the tibia may be missing. Near this position (e.g., a region within aspatial distance threshold from the position), a missing region of thevessel of the tibia may be selected. In some embodiments, a y coordinatefor the missing region of the vessel of the tibia may be selected assmall as possible (e.g., a position including a minimum y coordinatenear the missing region of the vessel of the tibia is selected along thedirection from the front to the rear of a subject), and thecorresponding z coordinate may be selected as large as possible (e.g., aposition including a minimum z coordinate near the missing region of thevessel of the tibia is selected along the direction from the foot to theknee of the measured subject). According to such a selecting way, if twomissing regions of the vessel of the tibia are selected at the left legand the right leg respectively, the regions may be data supplementregions of the vessel of the tibia.

In 1003, the first vessel generation sub-unit 901 may obtain supplementpoints of one or more vessel data at a data supplement region, based onthe image data (e.g., the image data of the crus site obtained in 611).In some embodiments, a source distance field may be made to reach aprimary part of the vessel, and pixels (or voxels) where the primarypart of the vessel is reached may be set as a data supplement point in adata supplement region. For example, the data supplement point mayinclude a start point, an end point, and a path point of a vessel.

In 1004, based on the data supplement point obtained in 1003, the firstvessel generation sub-unit 901 may supplement data representing thecenter line of the vessel in the broken vessel data. In someembodiments, data of the center line of a vessel may be obtained basedon single-source shortest path algorithms. The single-source shortestpath algorithms for obtaining the center line of the vessel may includea Dijkstra algorithm, a Bellman-Ford algorithm, an A* searchingalgorithm, a Floyd-Warshall algorithm, a Johnson algorithm, a Viterbialgorithm, or the like. Taking the Dijkstra algorithm as an example, thevalue function in this algorithm may be derived from the distancetransformation of the supplement region and the distance between datasupplement points.

In 1005, the first vessel generation sub-unit 901 may supplement othervessel data based on the data of the center line of the vessel obtainedin 1004. In some embodiments, the first vessel generation sub-unit 901may select a certain region, outward with the center line of the vesselas a center, as a region of the vessel to be segmented, the center linemay be set as a preliminary segmentation result, and the boundary valuesof the vessel in the original image and at least one characteristicimage may be defined as vessel model conditions. In some embodiments,the characteristic image may refer to a processed image based on theoriginal image. The process may include image transformation, imageenhancement, image characteristic extraction, or the like, or acombination thereof. In some embodiments, the image characteristics mayinclude color characteristics, texture characteristics, shapecharacteristics, spatial relation characteristics, or the like, or acombination thereof. Regarding a point in the data of the center line ofthe vessel obtained in 1004 as the seed point, and the vessel may beidentified or extracted by judging that whether a value of an angiogramimage of a point near the seed point satisfies the boundary value of thecurrent vessel model condition.

FIG. 10-B is a flowchart illustrating an exemplary process forsupplementing foot vessel data according to some embodiments of thepresent disclosure. Supplementing the foot vessel data may be performedby the second vessel generation sub-unit 902. In 1011, the second vesselgeneration sub-unit 902 may identify the missing data point representingthe vessel of the foot in the broken vessel data.

In 1012, the second vessel generation sub-unit 902 may determine a datasupplement region based on the data missing site representing the vesselof the foot obtained in 1011. In some embodiments, the second vesselgeneration sub-unit 902 may determine ½ of the data missing points wherethe foot may be located in the image data (e.g., the image data of thecrus site obtained in 611) as data supplement regions.

In 1013, the second vessel generation sub-unit 902 may rank the datasupplement regions obtained in 1012. For example, the second vesselgeneration sub-unit 902 may rank the data supplement regions obtained in1012 in descending order of the average values of the coordinates in thez-direction (e.g., along the direction from the foot to the knee of asubject: the closer to the foot, the smaller the z coordinate is; thecloser to the knee, the larger the z coordinate is).

In 1014, the second vessel generation sub-unit 902 may perform asupplement operation on the data supplement region based on the rank in1013. In some embodiments, the higher average value of the z coordinatesin the data supplement region is, the higher priority of the supplementoperation is; the lower average value of the z coordinates in the datasupplement region is, the lower priority of the supplement operation is.The average value of the z coordinates may refer to an average value ofz-direction coordinates of multiple pixels (or voxels) in a datasupplement region.

FIG. 10-C is a flowchart illustrating an exemplary process forperforming a supplement operation on a data supplement region accordingto some embodiments of the present disclosure. In 1021, the secondvessel generation sub-unit 902 may obtain a series of supplement pointsat a data supplement region, based on the image data (e.g., the imagedata of the crus site obtained in 611).

In 1022, the second vessel generation sub-unit 902 may supplement datarepresenting the center line of the vessel in the broken vessel data,based on the data supplement points obtained in 1021. In someembodiments, the data of the center line of the vessel may be obtainedbased on single-source shortest path algorithms. The single-sourceshortest path algorithms for obtaining the center line of the vessel mayinclude a Dijkstra algorithm, a Bellman-Ford algorithm, an A* searchingalgorithm, a Floyd-Warshall algorithm, a Johnson algorithm, a Viterbialgorithm, etc. Taking the Dijkstra algorithm as an example, the valuefunction in this algorithm may be derived from the distancetransformation of the supplement region and the distance between datasupplement points.

In 1023, the second vessel generation sub-unit 902 may supplement othervessel data based on the data of the center line of the vessel obtainedin 1022. In some embodiments, the second vessel generation sub-unit 902may select a region, outward with the center line of the vessel as acenter, as a region of the vessel to be segmented, the center line maybe determined as a preliminary segmentation result, and the boundaryvalue of the vessel in the original image and at least onecharacteristic image may be defined as vessel model conditions. In someembodiments, the characteristic image may refer to the processed imagebased on the original image. The process may include imagetransformation, image enhancement, image characteristic extraction, orthe like, or a combination thereof. In some embodiments, the imagecharacteristics may include color characteristics, texturecharacteristics, shape characteristics, spatial relationcharacteristics, or the like, or a combination thereof. Regarding apoint in the data of the center line of the vessel obtained in 1022 asthe seed point, and the vessel may be identified or extracted by judgingthat whether a value of an angiogram image of a point near the seedpoint satisfies the boundary value of the current vessel modelcondition.

FIG. 10-D is a flowchart illustrating an exemplary process forsupplementing ilium vessel data according to some embodiments of thepresent disclosure. Supplementing the ilium vessel data may be performedby the third vessel generation sub-unit 903. In 1031, the third vesselgeneration sub-unit 903 may position the ilium segment in the imagebased on the obtained image data (e.g., the image data of the lower limbobtained in 611). Then, the region where the ilium segment is positionedmay be preprocessed. In some embodiments, the third vessel generationsub-unit 903 may leave a blank for several middle layers of thepositioned ilium segment and may expand the topmost layers (e.g., 1 to mlayers) and the lowermost layers (e.g., 1 to n layers) of the iliumsegment outward in the x-direction (e.g., along the direction from theright ilium to the left ilium of a subject) for attaining a point with afirst gray-scale value smaller than a threshold (e.g., 100). Theexpanded ilium segment may be set as a region where the vessel cannotgrow in 1035 (i.e., a growth control data set) to prevent the centerline of the vessel grown in 1035 from passing through the ilium.

In 1032, the third vessel generation sub-unit 903 may identify the datamissing point representing the vessel of the foot in the broken vesseldata.

In 1033, the third vessel generation sub-unit 903 may set datasupplement regions based on the data missing site representing thevessel of the ilium obtained in 1012. In some embodiments, the thirdvessel generation sub-unit 903 may set the broken vessel data near therange (e.g., a region within a spatial distance threshold from the ycoordinate) of the y-direction coordinate where the ilium may be located(e.g., along the direction from the front to the rear of a subject, thecloser to the front, the smaller the y coordinate is; the closer to therear, the larger the y coordinate is) as data supplement regions, sothat selection of crush bones near the vertebra may be avoided ordecreased.

In 1034, the third vessel generation sub-unit 903 may rank the datasupplement regions obtained in 1033. For example, the third vesselgeneration sub-unit 903 may assign a higher weight to a data supplementregion where the x coordinate (e.g., along the direction from the rightilium to the left ilium of a subject: the closer to the right ilium, thesmaller the x coordinate is) is closer to the center, the y coordinate(e.g., along the direction from the front to the rear of a subject: thecloser to the front, the smaller the y coordinate is; the closer to therear, the larger the y coordinate is) is lager, and obtain a value onthe basis of which a ranking in the descending order may be performed todetermine the rank of each data supplement region. The average value ofx coordinates may refer to an average value of x-direction coordinatesof multiple pixels (or voxels) in the data supplement region. Theaverage value of the y coordinates may refer to an average value ofy-direction coordinates of multiple pixels (or voxels) in the datasupplement region. The rank of data supplement regions may refer to theorder of supplementing data in the course of data supplementation.

In 1035, the third vessel generation sub-unit 903 may perform thesupplement operation on the data supplement region based on the orderranked in 1034. The supplementation method may perform the supplementoperation on the data supplement region as shown in FIG. 10-C.

FIG. 10-E is a flowchart illustrating an exemplary process forgenerating a vessel according to some embodiments of the presentdisclosure. The process for generating the vessel may includedetermining a value of a boundary distance field 1041, extracting acenter line of a vessel 1042, and obtaining a vessel extraction result1043. The vessel may be generated by the fourth vessel generationsub-unit 904. It should be noted that the generation of the vessel ofthe tibia, the vessel of the foot, and/or the vessel of the ilium mayalso use the process as shown in FIG. 10-E.

In 1041, a value of a boundary distance field of the pixels/voxels ofthe connected domain in the second subtraction image may be determined.The computing methods of the value of the boundary distance field valuemay be described above. In some embodiments, the fourth vesselgeneration sub-unit 904 may determine one or more connected domains inthe second subtraction image based on the value of the boundary distancefield value.

In 1042, the center line of the vessel may be extracted. In someembodiments, the fourth vessel generation sub-unit 904 may extract thecenter line of the vessel based on the value of the boundary distancefield determined in 1041 and the shortest path algorithms. In someembodiments, the fourth vessel generation sub-unit 904 may determinegrowing points of one or more center lines of the vessel based on thevalue of the boundary distance field for extracting the center line ofthe vessel based on the growing point. For example, the fourth vesselgeneration sub-unit 904 may obtain a first vessel growing point and asecond vessel growing point based on the value of the boundary distancefield, and extract the center line of the vessel by using the shortestpath algorithms, based on the first vessel growing point and the secondvessel growing point. In some embodiments, the fourth vessel generationsub-unit 904 may exclude the skeleton region in the second skeleton maskto extract the center line of the vessel. This means that the skeletonregion in the second skeleton mask may be bypassed in the process ofextracting the center line of the vessel. This means that the extractedcenter line of the vessel may not pass through the skeleton region. Theshortest path algorithm may include a Dijkstra algorithm, a Bellman-Fordalgorithm, an A* searching algorithm, a Floyd-Warshall algorithm, aJohnson algorithm, a Viterbi algorithm, or the like, or a combinationthereof.

In 1043, the vessel may be generated based on the center line of thevessel to obtain an extraction result of the vessel. In someembodiments, the fourth vessel generation sub-unit 904 may select one ormore pixels/voxels in the center line of the vessel as the seedpoint(s). In some embodiments, the regional growth may be performedbased on the seed point to obtain the extraction result of the vessel.Similar to 1042, the vessel growth may bypass the skeleton region.

FIG. 11 is a flowchart illustrating an exemplary process for positioninga specific site of a human body in a medicine image according to someembodiments of the present disclosure. The method may achievepositioning by comparing some characteristics of each layer in a dataset of a template image and in a data set of an image to be positioned.

In 1101, the data processing system 130 may generate a template imagerepresenting a target site. In some embodiments, the template image mayinclude one or more template histograms. In a template image, somecharacteristics, for example, f_(i), iεn may be extracted for a layerwhere the identified position may be located, and each characteristicmay be formed as a template histogram T_(i), iεn along the z-direction.The template image may refer to an image including a region where thetarget site may be located. For example, if what is to be positioned isan ilium, the template image may include an image of a region where theilium may be located. In some embodiments, the template image and theimage to be positioned may come from different measured subjects. Thecharacteristics may include the number of pixels (or voxels) in acertain gray scale range, the sectional area of the bone, or the like,or a combination thereof. The selected characteristics may includecertain invariance. The invariance may refer that the difference amongthe characteristics of different measured subjects is small (e.g., thedifference may be within a certain threshold range). For example, sinceevery tissue in the CT angiographic image includes a specific gray-scalevalue, the number of pixels (or voxels) in certain gray scales range ineach layer may include certain invariance for different measuredsubjects. In some embodiments, the data processing system 130 maygenerate a template image based on a reference image. The referenceimage may include image data similar to those of the sites of themeasured subject in the image to be positioned. Specifically, the dataprocess system 130 may obtain reference image data, extractcharacteristics of the reference image data, and generate a templateimage based on the characteristics.

In 1102, the data processing system 130 may obtain the image to bepositioned, extract one or more characteristics identical to thecharacteristics in 1101 for each layer, and generate an image histogramh_(i), iεn to be positioned for each characteristic along the zdirection (e.g., along the direction from the foot to the head of thesubject: the closer to the foot, the smaller the z coordinate is; thecloser to the head, the larger the z coordinate is).

In 1103, the data processing system 130 may compare the image histogramto be positioned with the template histogram. For each characteristic,the corresponding template histogram T_(i) may be positioned above theh_(i) to perform moving comparison; every time when the templatehistogram is moved to a position, the template histogram T_(i) may bestretched and the similarity degree of the image histogram to bepositioned and the template histogram may be calculated, based on thecorresponding local maximum values and the corresponding local minimumvalues. The position with a maximum similarity degree is the position ofthe site to be positioned. The similarity degree may be calculated basedon the difference between the characteristics of the position where theimage histogram to be positioned and the template histogram may beoverlapped. For example, the sum of the absolute values ofcharacteristic difference values of a position may be divided by thezoom factor of the template to obtain the local similarity degreecorresponding to the characteristic. Further, the sum of localsimilarity degrees corresponding to two or more characteristics may bedesignated as a total local similarity degree, so that a positionincluding the maximum total local similarity degree may be designated asa position where the specific site may be positioned.

EXAMPLES

The following embodiments are for illustration purposes and are notintended to limit the scope of the present disclosure as set forth inthe embodiments.

Example 1

FIG. 12 is a schematic diagram illustrating exemplary results of fivevessels of lower limbs according to some embodiments of the presentdisclosure. A first lower limb vessel 1201, a second lower limb vessel1202, a third lower limb vessel 1203, a fourth lower limb vessel 1204,and a fifth lower limb vessel 1205 were obtained from image data of fivedifferent human subjects. In the process for extracting the vessels ofthe lower limbs shown in FIG. 12, processes of skeleton extraction,broken vessel segment extraction, and vessel generation (e.g., themethod shown in FIG. 6-B) were used, and different parameters were usedin the specific process operation. A vessel generation technique (e.g.,the processes shown in FIGS. 10-A, 10-B, and 10-C) were used in somesites (e.g., a crus, an ilium, or a foot bone).

Example 2

FIG. 13 is a schematic diagram illustrating an exemplary second vesselmask according to some embodiments of the present disclosure. The secondvessel mask is the result obtained according to the process shown inFIG. 7-E. As shown in FIG. 13, the second vessel mask may include mostof the vascular tissues.

Example 3

FIG. 14 is a schematic diagram illustrating an exemplary result ofvessel extraction according to some embodiments of the presentdisclosure. The vessel shown in FIG. 14 is the result obtained accordingto the process shown in FIG. 10-E.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment,” “one embodiment,” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 1703, Perl, COBOL1702, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (e.g., through the Internet using an Internet ServiceProvider) or in a cloud computing environment or offered as a servicesuch as a software as a service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software-only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, used to describeand claim certain embodiments of the application are to be understood asbeing modified in some instances by the term “about,” “approximate,” or“substantially.” For example, “about,” “approximate,” or “substantially”may indicate ±20% variation of the value it describes, unless otherwisestated. Accordingly, in some embodiments, the numerical parameters setforth in the written description and attached claims are approximationsthat may vary depending upon the desired properties sought to beobtained by a particular embodiment. In some embodiments, the numericalparameters should be construed in light of the number of reportedsignificant digits and by applying ordinary rounding techniques.Notwithstanding that the numerical ranges and parameters setting forththe broad scope of some embodiments of the application areapproximations, the numerical values set forth in the specific examplesare reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1. A method, comprising: obtaining an original image including aplurality of image data, each of the plurality of image datacorresponding to a pixel (or a voxel), the plurality of image dataincluding a target data set, the target data set representing a firststructure; extracting a first reference data set from the plurality ofimage data, the first reference data set including the target data setand a second reference data set, the second reference data set includingdata of a second structure; extracting the second reference data setfrom the plurality of image data; and obtaining the target data setbased on the first reference data set and the second reference data set.2. The method of claim 1, the obtaining the target data set comprising:obtaining a frame data set based on the first reference data set and thesecond reference data set, the frame data set being a subset of thetarget data set; and performing at least one data supplement operationon the frame data set to obtain the target data set.
 3. The method ofclaim 2, wherein the first structure includes a vessel, the secondstructure includes a skeleton, the target data set includes vessel data,the first reference data set includes vessel data and skeleton data, thesecond reference data set includes skeleton data, and the frame data setincludes data of broken vessel segments.
 4. The method of claim 3, theextracting the first reference data set comprising: determining at leastone connected domain CD1 in the original image; determining a first seedpoint based on the at least one connected domain CD1; and performing aregional growth on the original image based on the first seed point anda first threshold to obtain a first image, wherein pixels or voxels inthe first image and data in the first reference data set are bijective.5. The method of claim 4, the determining the first seed pointcomprising: determining values of a boundary distance field of the atleast one connected domain CD1 to obtain a data set pfield-1;determining a circularity degree of the at least one connected domainCD1 based on the data set pfield-1; determining a target connecteddomain based on the at least one connected domain CD1; and determiningthe first seed point based on the target connected domain.
 6. The methodof claim 5, the determining the circularity degree of the at least oneconnected domain CD1 comprising: determining a radius of the at leastone connected domain CD1 based on the data set pfield-1; determining acircular area of the at least one connected domain CD1 based on theradius of the at least one connected domain CD1; and determining thecircularity degree of the at least one connected domain CD1 based on thecircular area of the at least one connected domain CD1 and an actualarea of the at least one connected domain CD1.
 7. The method of claim 5,the extracting the second reference data set comprises: calculating aboundary distance field based on the first image to obtain a data setpfield-2; segmenting the first structure based on the data set pfield-2to obtain a vessel mask; subtracting the vessel mask from the firstimage to obtain a first subtraction image; segmenting the secondstructure based on the first subtraction image to obtain a firstskeleton mask; and obtaining a second skeleton mask based on theoriginal image and the first skeleton mask, wherein pixels or voxels inthe second skeleton mask and data in the second reference data set arebijective.
 8. The method of claim 7, the obtaining the vessel maskcomprising: performing a regional growth on the first image based on thefirst seed point to obtain a first vessel mask; and dilating the firstvessel mask to obtain the vessel mask.
 9. The method of claim 7, theobtaining the first skeleton mask comprising: calculating a boundarydistance field based on the first subtraction image to obtain a data setpfield-3; determining a skeleton seed point based on the data setpfield-3; and performing a regional growth on the first subtractionimage based on the skeleton seed point to obtain the first skeletonmask.
 10. The method of claim 7, the obtaining the second skeleton maskcomprising: determining a first skeleton region based on the firstskeleton mask; and dilating the first skeleton region to obtain a firsttemporary skeleton mask.
 11. The method of claim 10, the obtaining thesecond skeleton mask further comprising: performing a regional growth onthe original image based on a second threshold to obtain a second image;filling the second image to obtain a filled second image; obtaining asuperimposition image based on the first temporary skeleton mask and thefilled second image; and performing a closing operation on at least oneconnected domain CD2 in the superimposition image to obtain the secondskeleton mask.
 12. The method of claim 10, the determining the firstskeleton region comprising: eroding the first skeleton mask to obtain atleast one connected domain CD3; and determining the first skeletonregion based on the at least one connected domain CD3.
 13. The method ofclaim 12, the determining the first skeleton region further comprising:designating a connected domain with the maximum area or the maximumvolume in the at least one connected domain CD3 as the first skeletonarea.
 14. The method of claim 7, the obtaining the frame data setcomprising: subtracting the second skeleton mask from the first image toobtain a second subtraction image, wherein pixels or voxels in thesecond subtraction image and data in the frame data set are bijective.15. The method of claim 3, the performing at least one data supplementoperation on the frame data set comprising: selecting a second seedpoint from the frame data set; and performing a regional growth based ona second threshold and the second seed point to obtain the target dataset.
 16. The method of claim 14, the performing at least one datasupplement operation on the frame data set comprising: extracting acenter line of a vessel based on the second subtraction image; andgenerating the vessel based on the center line of the vessel.
 17. Themethod of claim 16, the extracting the center line of the vesselcomprising: calculating a boundary distance field based on the secondsubtraction image; obtaining a first vessel growing point and a secondvessel growing point based on the boundary distance field; andextracting the center line of the vessel using the shortest routealgorithm based on the first vessel growing point and the second vesselgrowing point.
 18. The method of claim 17, the extracting the centerline of the vessel further comprising: determining a second skeletonregion in the second skeleton mask; and extracting the center line ofthe vessel by excluding the second skeleton region. 19.-25. (canceled)26. A non-transitory computer readable medium including executableinstructions that, when executed by at least one processor, cause the atleast one processor to effectuate a method comprising: obtaining animage including a plurality of image data, each of the plurality ofimage data corresponding to a pixel (or a voxel), the plurality of imagedata including a target data set, the target data set representing afirst structure; extracting a first reference data set from theplurality of image data, the first reference data set including thetarget data set and a second reference data set, the second referencedata set including data of a second structure; extracting the secondreference data set from the plurality of image data; and obtaining thetarget data set based on the first reference data set and the secondreference data set.
 27. A system comprising: at least one processor; andexecutable instructions that, when executed by the at least oneprocessor, causes the at least one processor to effectuate a methodcomprising: obtaining an original image including a plurality of imagedata, each of the plurality of image data corresponding to a pixel (or avoxel), the plurality of image data including a target data set, thetarget data set representing a first structure; extracting a firstreference data set from the plurality of image data, the first referencedata set including the target data set and a second reference data set,the second reference data set including data of a second structure;extracting the second reference data set from the plurality of imagedata; and obtaining the target data set based on the first referencedata set and the second reference data set.
 28. (canceled)